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tomhodemon/fever-query_encoder-lora-bsz16-77588-gradacc1	tomhodemon	2023-12-03T03:33:25Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google-bert/bert-base-cased", "base_model:adapter:google-bert/bert-base-cased", "region:us" ]	null	2023-12-03T03:33:24Z	--- library_name: peft base_model: bert-base-cased --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2
pijarcandra22/BartIndo2Bali	pijarcandra22	2023-12-03T03:25:16Z	3	0	transformers	[ "transformers", "tf", "bart", "text2text-generation", "generated_from_keras_callback", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-03T02:39:31Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_keras_callback model-index: - name: pijarcandra22/BartIndo2Bali results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # pijarcandra22/BartIndo2Bali This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1151 - Validation Loss: 2.6202 - Epoch: 99 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.3767 \| 3.6194 \| 0 \| \| 3.5364 \| 3.1996 \| 1 \| \| 3.1525 \| 2.9458 \| 2 \| \| 2.8777 \| 2.8118 \| 3 \| \| 2.6993 \| 2.6979 \| 4 \| \| 2.5550 \| 2.6071 \| 5 \| \| 2.4536 \| 2.5362 \| 6 \| \| 2.3338 \| 2.4572 \| 7 \| \| 2.2394 \| 2.3878 \| 8 \| \| 2.1466 \| 2.3692 \| 9 \| \| 2.0795 \| 2.3189 \| 10 \| \| 2.0061 \| 2.2674 \| 11 \| \| 1.9321 \| 2.2393 \| 12 \| \| 1.8837 \| 2.2181 \| 13 \| \| 1.8224 \| 2.2002 \| 14 \| \| 1.7626 \| 2.1671 \| 15 \| \| 1.7251 \| 2.1386 \| 16 \| \| 1.6624 \| 2.1245 \| 17 \| \| 1.6191 \| 2.1134 \| 18 \| \| 1.6177 \| 2.1061 \| 19 \| \| 1.5524 \| 2.0845 \| 20 \| \| 1.4965 \| 2.0750 \| 21 \| \| 1.4618 \| 2.0527 \| 22 \| \| 1.4188 \| 2.0584 \| 23 \| \| 1.3774 \| 2.0359 \| 24 \| \| 1.3469 \| 2.0567 \| 25 \| \| 1.3113 \| 2.0295 \| 26 \| \| 1.2791 \| 2.0134 \| 27 \| \| 1.2436 \| 2.0431 \| 28 \| \| 1.1915 \| 2.0201 \| 29 \| \| 1.1815 \| 2.0283 \| 30 \| \| 1.1314 \| 2.0230 \| 31 \| \| 1.1071 \| 2.0424 \| 32 \| \| 1.0781 \| 2.0357 \| 33 \| \| 1.0429 \| 2.0208 \| 34 \| \| 1.0134 \| 2.0458 \| 35 \| \| 0.9799 \| 2.0466 \| 36 \| \| 0.9567 \| 2.0592 \| 37 \| \| 0.9261 \| 2.0278 \| 38 \| \| 0.8931 \| 2.0641 \| 39 \| \| 0.8742 \| 2.0783 \| 40 \| \| 0.8397 \| 2.0781 \| 41 \| \| 0.8228 \| 2.1010 \| 42 \| \| 0.7819 \| 2.1042 \| 43 \| \| 0.7667 \| 2.1302 \| 44 \| \| 0.7508 \| 2.1193 \| 45 \| \| 0.7136 \| 2.1372 \| 46 \| \| 0.6849 \| 2.1513 \| 47 \| \| 0.6625 \| 2.1747 \| 48 \| \| 0.6451 \| 2.1936 \| 49 \| \| 0.6114 \| 2.1650 \| 50 \| \| 0.5907 \| 2.2176 \| 51 \| \| 0.5781 \| 2.2313 \| 52 \| \| 0.5594 \| 2.2287 \| 53 \| \| 0.5361 \| 2.2260 \| 54 \| \| 0.5168 \| 2.2444 \| 55 \| \| 0.5022 \| 2.2660 \| 56 \| \| 0.4826 \| 2.2912 \| 57 \| \| 0.4607 \| 2.2922 \| 58 \| \| 0.4442 \| 2.2912 \| 59 \| \| 0.4262 \| 2.3032 \| 60 \| \| 0.4050 \| 2.3335 \| 61 \| \| 0.4005 \| 2.3327 \| 62 \| \| 0.3826 \| 2.3379 \| 63 \| \| 0.3658 \| 2.3369 \| 64 \| \| 0.3442 \| 2.3629 \| 65 \| \| 0.3384 \| 2.3887 \| 66 \| \| 0.3287 \| 2.3868 \| 67 \| \| 0.3140 \| 2.3609 \| 68 \| \| 0.3078 \| 2.4009 \| 69 \| \| 0.2953 \| 2.4071 \| 70 \| \| 0.2855 \| 2.4421 \| 71 \| \| 0.2715 \| 2.4290 \| 72 \| \| 0.2647 \| 2.4227 \| 73 \| \| 0.2483 \| 2.4457 \| 74 \| \| 0.2402 \| 2.4582 \| 75 \| \| 0.2355 \| 2.4509 \| 76 \| \| 0.2272 \| 2.4788 \| 77 \| \| 0.2198 \| 2.4795 \| 78 \| \| 0.2077 \| 2.4786 \| 79 \| \| 0.1989 \| 2.5080 \| 80 \| \| 0.1992 \| 2.4929 \| 81 \| \| 0.1905 \| 2.5120 \| 82 \| \| 0.1880 \| 2.5345 \| 83 \| \| 0.1773 \| 2.5147 \| 84 \| \| 0.1734 \| 2.5270 \| 85 \| \| 0.1663 \| 2.5399 \| 86 \| \| 0.1618 \| 2.5581 \| 87 \| \| 0.1576 \| 2.5533 \| 88 \| \| 0.1550 \| 2.5177 \| 89 \| \| 0.1475 \| 2.5689 \| 90 \| \| 0.1453 \| 2.5720 \| 91 \| \| 0.1398 \| 2.5526 \| 92 \| \| 0.1357 \| 2.5638 \| 93 \| \| 0.1325 \| 2.5782 \| 94 \| \| 0.1293 \| 2.6026 \| 95 \| \| 0.1263 \| 2.6147 \| 96 \| \| 0.1257 \| 2.6056 \| 97 \| \| 0.1149 \| 2.6323 \| 98 \| \| 0.1151 \| 2.6202 \| 99 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0
FreedomIntelligence/OVM-llama2-7b	FreedomIntelligence	2023-12-03T03:22:41Z	0	1	null	[ "arxiv:2311.09724", "license:llama2", "region:us" ]	null	2023-12-01T04:25:10Z	--- license: llama2 --- The verifier model (`/llama7b-2-ep2-n100-scahead-mse-lm-token`) and the generator model (`/llama7b-2-ep2`) in GSM8K, finetuned from Llama2-7B. See the Mistral-7B version in [OVM-Mistral-7b](https://huggingface.co/FreedomIntelligence/OVM-Mistral-7b). See the paper [Outcome-supervised Verifiers for Planning in Mathematical Reasoning](https://arxiv.org/pdf/2311.09724.pdf) and the code in [github](https://github.com/FreedomIntelligence/OVM)
TheBloke/loyal-piano-m7-GGUF	TheBloke	2023-12-03T03:16:20Z	123	4	transformers	[ "transformers", "gguf", "mistral", "en", "dataset:pankajmathur/orca_mini_v1_dataset", "dataset:openai/summarize_from_feedback", "dataset:PygmalionAI/PIPPA", "dataset:chargoddard/rpguild", "dataset:lemonilia/LimaRP", "base_model:chargoddard/loyal-piano-m7", "base_model:quantized:chargoddard/loyal-piano-m7", "license:cc-by-nc-4.0", "region:us" ]	null	2023-12-03T00:11:00Z	--- base_model: chargoddard/loyal-piano-m7 datasets: - pankajmathur/orca_mini_v1_dataset - openai/summarize_from_feedback - PygmalionAI/PIPPA - chargoddard/rpguild - lemonilia/LimaRP inference: false language: - en license: cc-by-nc-4.0 model_creator: Charles Goddard model_name: Loyal Piano M7 model_type: mistral prompt_template: '{prompt} ' quantized_by: TheBloke tags: - mistral --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Loyal Piano M7 - GGUF - Model creator: [Charles Goddard](https://huggingface.co/chargoddard) - Original model: [Loyal Piano M7](https://huggingface.co/chargoddard/loyal-piano-m7) <!-- description start --> ## Description This repo contains GGUF format model files for [Charles Goddard's Loyal Piano M7](https://huggingface.co/chargoddard/loyal-piano-m7). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/loyal-piano-m7-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/loyal-piano-m7-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF) * [Charles Goddard's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/chargoddard/loyal-piano-m7) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Unknown ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [loyal-piano-m7.Q2_K.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q2_K.gguf) \| Q2_K \| 2 \| 3.08 GB\| 5.58 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [loyal-piano-m7.Q3_K_S.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \| \| [loyal-piano-m7.Q3_K_M.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [loyal-piano-m7.Q3_K_L.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [loyal-piano-m7.Q4_0.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [loyal-piano-m7.Q4_K_S.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [loyal-piano-m7.Q4_K_M.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [loyal-piano-m7.Q5_0.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [loyal-piano-m7.Q5_K_S.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [loyal-piano-m7.Q5_K_M.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [loyal-piano-m7.Q6_K.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [loyal-piano-m7.Q8_0.gguf](https://huggingface.co/TheBloke/loyal-piano-m7-GGUF/blob/main/loyal-piano-m7.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/loyal-piano-m7-GGUF and below it, a specific filename to download, such as: loyal-piano-m7.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/loyal-piano-m7-GGUF loyal-piano-m7.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/loyal-piano-m7-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/loyal-piano-m7-GGUF loyal-piano-m7.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m loyal-piano-m7.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./loyal-piano-m7.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "{prompt}", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./loyal-piano-m7.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Charles Goddard's Loyal Piano M7 [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) Experimenting with dataset ratios. Intended to be a roleplay-focused model with some smarts and good long-context recall. Not sure if I've succeeded on the roleplay front, but something sure went right! Currently the #4 7B model on the leaderboard as of 11/30/2023. Going to riff on this and see where it goes. \| model \| Average \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| Winogrande \| GSM8K \| DROP \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| fblgit/juanako-7b-UNA \| 59.91 \| 68.17 \| 85.34 \| 62.47 \| 65.13 \| 78.85 \| 20.7 \| 38.74 \| \| Intel/neural-chat-7b-v3-1 \| 59.06 \| 66.21 \| 83.64 \| 62.37 \| 59.65 \| 78.14 \| 19.56 \| 43.84 \| \| Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B \| 58.6 \| 66.55 \| 84.47 \| 63.34 \| 61.22 \| 78.37 \| 23.58 \| 32.66 \| \| chargoddard/loyal-piano-m7 \| 58.42 \| 66.72 \| 85.03 \| 64.43 \| 60.03 \| 79.08 \| 25.7 \| 27.92 \| \| Gryphe/MythoMist7b \| 58.26 \| 65.87 \| 83.55 \| 62.32 \| 59.98 \| 78.06 \| 20.24 \| 37.82 \| Dataset composition: \| dataset \| rows used \| percent of total \| \| --- \| --- \| --- \| \| PIPPA \| 14.6k \| 43% \| \| summarize_from_feedback \| 9k \| 26% \| \| orca_mini_v1_dataset \| 5.6k \| 17% \| \| rpguild \| 2.86k \| 8% \| \| LimaRP \| 2k \| 6% \| <!-- original-model-card end -->
openaccess-ai-collective/DPOpenHermes-11B	openaccess-ai-collective	2023-12-03T03:04:41Z	1,521	3	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:teknium/openhermes", "dataset:argilla/ultrafeedback-binarized-preferences", "dataset:Intel/orca_dpo_pairs", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-03T02:47:58Z	--- license: apache-2.0 datasets: - teknium/openhermes - argilla/ultrafeedback-binarized-preferences - Intel/orca_dpo_pairs language: - en library_name: transformers --- # DPOpenHermes 11B This is a mergekit merge of DPOpenHermes-7B from seperate versions of it. ``` slices: - sources: - model: openaccess-ai-collective/DPOpenHermes-7B revision: dpo-v0 layer_range: [0, 24] - sources: - model: openaccess-ai-collective/DPOpenHermes-7B layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ```
TheBloke/Inairtra-7B-GGUF	TheBloke	2023-12-03T02:48:06Z	99	3	transformers	[ "transformers", "gguf", "mistral", "base_model:Bronya-Rand/Inairtra-7B", "base_model:quantized:Bronya-Rand/Inairtra-7B", "license:apache-2.0", "region:us" ]	null	2023-12-02T23:52:32Z	--- base_model: Bronya-Rand/Inairtra-7B inference: false license: apache-2.0 model_creator: Azariel Del Carmen model_name: Inairtra 7B model_type: mistral prompt_template: '### System: {system_message} ### User: {prompt} ### Assistant: ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Inairtra 7B - GGUF - Model creator: [Azariel Del Carmen](https://huggingface.co/Bronya-Rand) - Original model: [Inairtra 7B](https://huggingface.co/Bronya-Rand/Inairtra-7B) <!-- description start --> ## Description This repo contains GGUF format model files for [Azariel Del Carmen's Inairtra 7B](https://huggingface.co/Bronya-Rand/Inairtra-7B). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Inairtra-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Inairtra-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Inairtra-7B-GGUF) * [Azariel Del Carmen's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Bronya-Rand/Inairtra-7B) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: System-User-Assistant ``` ### System: {system_message} ### User: {prompt} ### Assistant: ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [inairtra-7b.Q2_K.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q2_K.gguf) \| Q2_K \| 2 \| 3.08 GB\| 5.58 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [inairtra-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \| \| [inairtra-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [inairtra-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [inairtra-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [inairtra-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [inairtra-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [inairtra-7b.Q5_0.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [inairtra-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [inairtra-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [inairtra-7b.Q6_K.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [inairtra-7b.Q8_0.gguf](https://huggingface.co/TheBloke/Inairtra-7B-GGUF/blob/main/inairtra-7b.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Inairtra-7B-GGUF and below it, a specific filename to download, such as: inairtra-7b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Inairtra-7B-GGUF inairtra-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Inairtra-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Inairtra-7B-GGUF inairtra-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m inairtra-7b.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\n{system_message}\n### User:\n{prompt}\n### Assistant:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./inairtra-7b.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "### System:\n{system_message}\n### User:\n{prompt}\n### Assistant:", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./inairtra-7b.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Azariel Del Carmen's Inairtra 7B <p align="center"> <!-- <img src="./assets/Core1000AIIMG.png"/> --> <p align="center" style="font-size: 26px"><b>Inairtra-7B</b></p> <p align="center" style="font-size: 14px">Model Size: 7B</p> </p> <p align="center"> <img src="./assets/SmallBronyaLogo.png" style="width: 45%;"> </p> <p align="center" style="font-size: 20px">A <b>experimental</b> (and beginner) model merge using Intel's Neural Chat 7B</p> ## Model Details Trained on: *Intel Xeon E5-2693v3 \| NVIDIA RTX 2080 Ti \| 128 GB DDR4 (yes I'm poor :( )* The Inairtra-7B LLM is a LLM made by Bronya Rand (bronya_rand / Bronya-Rand) as a beginning learning model to merging models using [MergeKit](https://github.com/cg123/mergekit) and GGUF quantization. This model is based off Intel's [Neural Chat 7B V3.1](https://huggingface.co/Intel/neural-chat-7b-v3-1) as the base model along with three additional Mistral models. The Inairtra-7B architecture is based off: [Mistral*](https://huggingface.co/mistralai/Mistral-7B-v0.1) The models used to create the Inairtra-7B are as follows: - Intel's Neural Chat 7B V3.1 ([Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)) - Teknium's Airoboros Mistral 2.2 7B ([teknium/airoboros-mistral2.2-7b](https://huggingface.co/teknium/airoboros-mistral2.2-7b)) - Maywell's Synatra 7B V0.3 RP ([maywell/Synatra-7B-v0.3-RP](https://huggingface.co/maywell/Synatra-7B-v0.3-RP)) ## Prompt The Inairtra-7B should* (but unsure) support the same prompts as featured in Intel's Neural Chat, Airoboros Mistral and Synatra. ### For Intel ``` ### System: {system} ### User: {usr} ### Assistant: ``` ### For Airoboros ``` USER: <prompt> ASSISTANT: ``` ## Benchmarks? I have no idea how to do them. You are welcome to make your own. ## Ethical Considerations and Limitations The intended use-case for the Inairtra-7B LLM is for fictional writing/roleplay solely for personal entertainment purposes. Any other sort of usage outside of this is out of scope of my intentions and the LLM itself. The Inairtra-7B LLM has been merged with models which are uncensored/unfiltered. The LLM can produce content, including but not limited to, content that may be NSFW for those under the age of eighteen, content that may be illegal in certain states/countries, offensive content, etc. The Inairtra-7B LLM is not designed to produce the most accurate information. It may produce incorrect data like all other AI models. ### Disclaimer The license on this model does not constitute legal advice. I am not responsible for the actions of third parties (services/users/etc.) who use this model and distribute it for others. Please cosult an attorney before using this model for commercial purposes. <!-- original-model-card end -->
krich97/swin-tiny-patch4-window7-224-finetuned-eurosat	krich97	2023-12-03T02:27:47Z	36	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-03T01:54:44Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8111298482293423 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4435 - Accuracy: 0.8111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5077 \| 0.98 \| 41 \| 0.6378 \| 0.6796 \| \| 0.5111 \| 1.99 \| 83 \| 0.7097 \| 0.6577 \| \| 0.5395 \| 2.99 \| 125 \| 0.5374 \| 0.7470 \| \| 0.5498 \| 4.0 \| 167 \| 0.5524 \| 0.7420 \| \| 0.4754 \| 4.98 \| 208 \| 0.5324 \| 0.7639 \| \| 0.4662 \| 5.99 \| 250 \| 0.4962 \| 0.7639 \| \| 0.4677 \| 6.99 \| 292 \| 0.5070 \| 0.7774 \| \| 0.4525 \| 8.0 \| 334 \| 0.5144 \| 0.7673 \| \| 0.4635 \| 8.98 \| 375 \| 0.4978 \| 0.7757 \| \| 0.4309 \| 9.99 \| 417 \| 0.5388 \| 0.7774 \| \| 0.4292 \| 10.99 \| 459 \| 0.4937 \| 0.7825 \| \| 0.4182 \| 12.0 \| 501 \| 0.5234 \| 0.7808 \| \| 0.4242 \| 12.98 \| 542 \| 0.4539 \| 0.7960 \| \| 0.4053 \| 13.99 \| 584 \| 0.5089 \| 0.7858 \| \| 0.4135 \| 14.99 \| 626 \| 0.4655 \| 0.8044 \| \| 0.3888 \| 16.0 \| 668 \| 0.4398 \| 0.8212 \| \| 0.3701 \| 16.98 \| 709 \| 0.4258 \| 0.8145 \| \| 0.3641 \| 17.99 \| 751 \| 0.4339 \| 0.8196 \| \| 0.3547 \| 18.99 \| 793 \| 0.4556 \| 0.7993 \| \| 0.3623 \| 19.64 \| 820 \| 0.4435 \| 0.8111 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
chargoddard/loyal-piano-m7-cdpo	chargoddard	2023-12-03T02:25:23Z	1,388	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "dataset:HuggingFaceH4/ultrafeedback_binarized", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T20:10:28Z	--- license: cc-by-nc-4.0 datasets: - HuggingFaceH4/ultrafeedback_binarized language: - en --- Trained for one epoch on ultrafeedback_binarized using cDPO. Evaluation pending. Some initial benchmark results: \| Task \|Version\| Metric \|Value \| \|Stderr\| \|---------\|------:\|--------\|-----:\|---\|-----:\| \|hellaswag\| 0\|acc \|0.6621\|± \|0.0047\| \| \| \|acc_norm\|0.8525\|± \|0.0035\| \|arc_challenge\| 0\|acc \|0.6348\|± \|0.0141\| \| \| \|acc_norm\|0.6698\|± \|0.0137\| \|winogrande\| 0\|acc \|0.7861\|± \|0.0115\| \|gsm8k\| 0\|acc \|0.5694\|± \|0.0136\|
audo/seamless-m4t-v2-large	audo	2023-12-03T02:24:59Z	145	16	seamless_communication	[ "seamless_communication", "safetensors", "seamless_m4t_v2", "automatic-speech-recognition", "audio-to-audio", "text-to-speech", "af", "am", "ar", "as", "az", "be", "bn", "bs", "bg", "ca", "cs", "zh", "cy", "da", "de", "el", "en", "et", "fi", "fr", "or", "om", "ga", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "ig", "id", "is", "it", "jv", "ja", "kn", "ka", "kk", "mn", "km", "ky", "ko", "lo", "ln", "lt", "lb", "lg", "lv", "ml", "mr", "mk", "mt", "mi", "my", "nl", "nb", "ne", "ny", "oc", "pa", "ps", "fa", "pl", "pt", "ro", "ru", "sk", "sl", "sn", "sd", "so", "es", "sr", "sv", "sw", "ta", "te", "tg", "tl", "th", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yo", "ms", "zu", "ary", "arz", "yue", "kea", "license:cc-by-nc-4.0", "region:us" ]	text-to-speech	2023-12-03T02:00:25Z	--- license: cc-by-nc-4.0 language: - af - am - ar - as - az - be - bn - bs - bg - ca - cs - zh - cy - da - de - el - en - et - fi - fr - or - om - ga - gl - gu - ha - he - hi - hr - hu - hy - ig - id - is - it - jv - ja - kn - ka - kk - mn - km - ky - ko - lo - ln - lt - lb - lg - lv - ml - mr - mk - mt - mi - my - nl - nb - ne - ny - oc - pa - ps - fa - pl - pt - ro - ru - sk - sl - sn - sd - so - es - sr - sv - sw - ta - te - tg - tl - th - tr - uk - ur - uz - vi - wo - xh - yo - ms - zu - ary - arz - yue - kea metrics: - bleu - wer - chrf inference: False tags: - automatic-speech-recognition - audio-to-audio - text-to-speech library_name: seamless_communication --- # SeamlessM4T v2 SeamlessM4T is our foundational all-in-one Massively Multilingual and Multimodal Machine Translation model delivering high-quality translation for speech and text in nearly 100 languages. SeamlessM4T models support the tasks of: - Speech-to-speech translation (S2ST) - Speech-to-text translation (S2TT) - Text-to-speech translation (T2ST) - Text-to-text translation (T2TT) - Automatic speech recognition (ASR). SeamlessM4T models support: - 🎤 101 languages for speech input. - 💬 96 Languages for text input/output. - 🔊 35 languages for speech output. 🌟 We are releasing SeamlessM4T v2, an updated version with our novel UnitY2 architecture. This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks. The v2 version of SeamlessM4T is a multitask adaptation of our novel UnitY2 architecture. Unity2 with its hierarchical character-to-unit upsampling and non-autoregressive text-to-unit decoding considerably improves over SeamlessM4T v1 in quality and inference speed. SeamlessM4T v2 is also supported by 🤗 Transformers, more on it [in the dedicated section below](#transformers-usage). ![SeamlessM4T architectures](seamlessm4t_arch.svg) ## SeamlessM4T models \| Model Name \| #params \| checkpoint \| metrics \| \| ------------------ \| ------- \| --------------------------------------------------------------------------------------- \| ------------------------------------------------------------------------------------ \| \| [SeamlessM4T-Large v2](https://huggingface.co/facebook/seamless-m4t-v2-large) \| 2.3B \| [checkpoint](https://huggingface.co/facebook/seamless-m4t-v2-large/blob/main/seamlessM4T_v2_large.pt) \| [metrics](https://dl.fbaipublicfiles.com/seamless/metrics/seamlessM4T_large_v2.zip) \| \| [SeamlessM4T-Large (v1)](https://huggingface.co/facebook/seamless-m4t-large) \| 2.3B \| [checkpoint](https://huggingface.co/facebook/seamless-m4t-large/blob/main/multitask_unity_large.pt) \| [metrics](https://dl.fbaipublicfiles.com/seamless/metrics/seamlessM4T_large.zip) \| \| [SeamlessM4T-Medium (v1)](https://huggingface.co/facebook/seamless-m4t-medium) \| 1.2B \| [checkpoint](https://huggingface.co/facebook/seamless-m4t-medium/blob/main/multitask_unity_medium.pt) \| [metrics](https://dl.fbaipublicfiles.com/seamless/metrics/seamlessM4T_medium.zip) \| We provide the extensive evaluation results of seamlessM4T-Large and SeamlessM4T-Medium reported in the paper (as averages) in the `metrics` files above. The evaluation data ids for FLEURS, CoVoST2 and CVSS-C can be found [here](https://dl.fbaipublicfiles.com/seamless/metrics/evaluation_data_ids.zip) ## Evaluating SeamlessM4T models To reproduce our results or to evaluate using the same metrics over your own test sets, please check out the [Evaluation README here](https://github.com/facebookresearch/seamless_communication/tree/main/src/seamless_communication/cli/m4t/evaluate). ## Finetuning SeamlessM4T models Please check out the [Finetuning README here](https://github.com/facebookresearch/seamless_communication/tree/main/src/seamless_communication/cli/m4t/finetune). ## Transformers usage SeamlessM4T is available in the 🤗 Transformers library, requiring minimal dependencies. Steps to get started: 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) from main and [sentencepiece](https://github.com/google/sentencepiece): ``` pip install git+https://github.com/huggingface/transformers.git sentencepiece ``` 2. Run the following Python code to generate speech samples. Here the target language is Russian: ```py from transformers import AutoProcessor, SeamlessM4Tv2Model import torchaudio processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large") model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large") # from text text_inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt") audio_array_from_text = model.generate(text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze() # from audio audio, orig_freq = torchaudio.load("https://www2.cs.uic.edu/~i101/SoundFiles/preamble10.wav") audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16_000) # must be a 16 kHz waveform array audio_inputs = processor(audios=audio, return_tensors="pt") audio_array_from_audio = model.generate(audio_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze() ``` 3. Listen to the audio samples either in an ipynb notebook: ```py from IPython.display import Audio sample_rate = model.sampling_rate Audio(audio_array_from_text, rate=sample_rate) # Audio(audio_array_from_audio, rate=sample_rate) ``` Or save them as a `.wav` file using a third-party library, e.g. `scipy`: ```py import scipy sample_rate = model.sampling_rate scipy.io.wavfile.write("out_from_text.wav", rate=sample_rate, data=audio_array_from_text) # scipy.io.wavfile.write("out_from_audio.wav", rate=sample_rate, data=audio_array_from_audio) ``` For more details on using the SeamlessM4T model for inference using the 🤗 Transformers library, refer to the [SeamlessM4T v2 docs](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t_v2) or to this hands-on [Google Colab](https://colab.research.google.com/github/ylacombe/scripts_and_notebooks/blob/main/v2_seamless_m4t_hugging_face.ipynb). ## Supported Languages: Listed below, are the languages supported by SeamlessM4T-large (v1/v2). The `source` column specifies whether a language is supported as source speech (`Sp`) and/or source text (`Tx`). The `target` column specifies whether a language is supported as target speech (`Sp`) and/or target text (`Tx`). \| code \| language \| script \| Source \| Target \| \| ---- \| ---------------------- \| ---------- \| ------ \| ------ \| \| afr \| Afrikaans \| Latn \| Sp, Tx \| Tx \| \| amh \| Amharic \| Ethi \| Sp, Tx \| Tx \| \| arb \| Modern Standard Arabic \| Arab \| Sp, Tx \| Sp, Tx \| \| ary \| Moroccan Arabic \| Arab \| Sp, Tx \| Tx \| \| arz \| Egyptian Arabic \| Arab \| Sp, Tx \| Tx \| \| asm \| Assamese \| Beng \| Sp, Tx \| Tx \| \| ast \| Asturian \| Latn \| Sp \| \-- \| \| azj \| North Azerbaijani \| Latn \| Sp, Tx \| Tx \| \| bel \| Belarusian \| Cyrl \| Sp, Tx \| Tx \| \| ben \| Bengali \| Beng \| Sp, Tx \| Sp, Tx \| \| bos \| Bosnian \| Latn \| Sp, Tx \| Tx \| \| bul \| Bulgarian \| Cyrl \| Sp, Tx \| Tx \| \| cat \| Catalan \| Latn \| Sp, Tx \| Sp, Tx \| \| ceb \| Cebuano \| Latn \| Sp, Tx \| Tx \| \| ces \| Czech \| Latn \| Sp, Tx \| Sp, Tx \| \| ckb \| Central Kurdish \| Arab \| Sp, Tx \| Tx \| \| cmn \| Mandarin Chinese \| Hans \| Sp, Tx \| Sp, Tx \| \| cmn_Hant \| Mandarin Chinese \| Hant \| Sp, Tx \| Sp, Tx \| \| cym \| Welsh \| Latn \| Sp, Tx \| Sp, Tx \| \| dan \| Danish \| Latn \| Sp, Tx \| Sp, Tx \| \| deu \| German \| Latn \| Sp, Tx \| Sp, Tx \| \| ell \| Greek \| Grek \| Sp, Tx \| Tx \| \| eng \| English \| Latn \| Sp, Tx \| Sp, Tx \| \| est \| Estonian \| Latn \| Sp, Tx \| Sp, Tx \| \| eus \| Basque \| Latn \| Sp, Tx \| Tx \| \| fin \| Finnish \| Latn \| Sp, Tx \| Sp, Tx \| \| fra \| French \| Latn \| Sp, Tx \| Sp, Tx \| \| fuv \| Nigerian Fulfulde \| Latn \| Sp, Tx \| Tx \| \| gaz \| West Central Oromo \| Latn \| Sp, Tx \| Tx \| \| gle \| Irish \| Latn \| Sp, Tx \| Tx \| \| glg \| Galician \| Latn \| Sp, Tx \| Tx \| \| guj \| Gujarati \| Gujr \| Sp, Tx \| Tx \| \| heb \| Hebrew \| Hebr \| Sp, Tx \| Tx \| \| hin \| Hindi \| Deva \| Sp, Tx \| Sp, Tx \| \| hrv \| Croatian \| Latn \| Sp, Tx \| Tx \| \| hun \| Hungarian \| Latn \| Sp, Tx \| Tx \| \| hye \| Armenian \| Armn \| Sp, Tx \| Tx \| \| ibo \| Igbo \| Latn \| Sp, Tx \| Tx \| \| ind \| Indonesian \| Latn \| Sp, Tx \| Sp, Tx \| \| isl \| Icelandic \| Latn \| Sp, Tx \| Tx \| \| ita \| Italian \| Latn \| Sp, Tx \| Sp, Tx \| \| jav \| Javanese \| Latn \| Sp, Tx \| Tx \| \| jpn \| Japanese \| Jpan \| Sp, Tx \| Sp, Tx \| \| kam \| Kamba \| Latn \| Sp \| \-- \| \| kan \| Kannada \| Knda \| Sp, Tx \| Tx \| \| kat \| Georgian \| Geor \| Sp, Tx \| Tx \| \| kaz \| Kazakh \| Cyrl \| Sp, Tx \| Tx \| \| kea \| Kabuverdianu \| Latn \| Sp \| \-- \| \| khk \| Halh Mongolian \| Cyrl \| Sp, Tx \| Tx \| \| khm \| Khmer \| Khmr \| Sp, Tx \| Tx \| \| kir \| Kyrgyz \| Cyrl \| Sp, Tx \| Tx \| \| kor \| Korean \| Kore \| Sp, Tx \| Sp, Tx \| \| lao \| Lao \| Laoo \| Sp, Tx \| Tx \| \| lit \| Lithuanian \| Latn \| Sp, Tx \| Tx \| \| ltz \| Luxembourgish \| Latn \| Sp \| \-- \| \| lug \| Ganda \| Latn \| Sp, Tx \| Tx \| \| luo \| Luo \| Latn \| Sp, Tx \| Tx \| \| lvs \| Standard Latvian \| Latn \| Sp, Tx \| Tx \| \| mai \| Maithili \| Deva \| Sp, Tx \| Tx \| \| mal \| Malayalam \| Mlym \| Sp, Tx \| Tx \| \| mar \| Marathi \| Deva \| Sp, Tx \| Tx \| \| mkd \| Macedonian \| Cyrl \| Sp, Tx \| Tx \| \| mlt \| Maltese \| Latn \| Sp, Tx \| Sp, Tx \| \| mni \| Meitei \| Beng \| Sp, Tx \| Tx \| \| mya \| Burmese \| Mymr \| Sp, Tx \| Tx \| \| nld \| Dutch \| Latn \| Sp, Tx \| Sp, Tx \| \| nno \| Norwegian Nynorsk \| Latn \| Sp, Tx \| Tx \| \| nob \| Norwegian Bokmål \| Latn \| Sp, Tx \| Tx \| \| npi \| Nepali \| Deva \| Sp, Tx \| Tx \| \| nya \| Nyanja \| Latn \| Sp, Tx \| Tx \| \| oci \| Occitan \| Latn \| Sp \| \-- \| \| ory \| Odia \| Orya \| Sp, Tx \| Tx \| \| pan \| Punjabi \| Guru \| Sp, Tx \| Tx \| \| pbt \| Southern Pashto \| Arab \| Sp, Tx \| Tx \| \| pes \| Western Persian \| Arab \| Sp, Tx \| Sp, Tx \| \| pol \| Polish \| Latn \| Sp, Tx \| Sp, Tx \| \| por \| Portuguese \| Latn \| Sp, Tx \| Sp, Tx \| \| ron \| Romanian \| Latn \| Sp, Tx \| Sp, Tx \| \| rus \| Russian \| Cyrl \| Sp, Tx \| Sp, Tx \| \| slk \| Slovak \| Latn \| Sp, Tx \| Sp, Tx \| \| slv \| Slovenian \| Latn \| Sp, Tx \| Tx \| \| sna \| Shona \| Latn \| Sp, Tx \| Tx \| \| snd \| Sindhi \| Arab \| Sp, Tx \| Tx \| \| som \| Somali \| Latn \| Sp, Tx \| Tx \| \| spa \| Spanish \| Latn \| Sp, Tx \| Sp, Tx \| \| srp \| Serbian \| Cyrl \| Sp, Tx \| Tx \| \| swe \| Swedish \| Latn \| Sp, Tx \| Sp, Tx \| \| swh \| Swahili \| Latn \| Sp, Tx \| Sp, Tx \| \| tam \| Tamil \| Taml \| Sp, Tx \| Tx \| \| tel \| Telugu \| Telu \| Sp, Tx \| Sp, Tx \| \| tgk \| Tajik \| Cyrl \| Sp, Tx \| Tx \| \| tgl \| Tagalog \| Latn \| Sp, Tx \| Sp, Tx \| \| tha \| Thai \| Thai \| Sp, Tx \| Sp, Tx \| \| tur \| Turkish \| Latn \| Sp, Tx \| Sp, Tx \| \| ukr \| Ukrainian \| Cyrl \| Sp, Tx \| Sp, Tx \| \| urd \| Urdu \| Arab \| Sp, Tx \| Sp, Tx \| \| uzn \| Northern Uzbek \| Latn \| Sp, Tx \| Sp, Tx \| \| vie \| Vietnamese \| Latn \| Sp, Tx \| Sp, Tx \| \| xho \| Xhosa \| Latn \| Sp \| \-- \| \| yor \| Yoruba \| Latn \| Sp, Tx \| Tx \| \| yue \| Cantonese \| Hant \| Sp, Tx \| Tx \| \| zlm \| Colloquial Malay \| Latn \| Sp \| \-- \| \| zsm \| Standard Malay \| Latn \| Tx \| Tx \| \| zul \| Zulu \| Latn \| Sp, Tx \| Tx \| Note that seamlessM4T-medium supports 200 languages in the text modality, and is based on NLLB-200 (see full list in [asset card](https://github.com/facebookresearch/seamless_communication/blob/main/src/seamless_communication/cards/unity_nllb-200.yaml)) ## Citation For SeamlessM4T v2, please cite : ```bibtex @inproceedings{seamless2023, title="Seamless: Multilingual Expressive and Streaming Speech Translation", author="{Seamless Communication}, Lo{\"i}c Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean Maillard, Ruslan Mavlyutov, Alice Rakotoarison, Kaushik Ram Sadagopan, Abinesh Ramakrishnan, Tuan Tran, Guillaume Wenzek, Yilin Yang, Ethan Ye, Ivan Evtimov, Pierre Fernandez, Cynthia Gao, Prangthip Hansanti, Elahe Kalbassi, Amanda Kallet, Artyom Kozhevnikov, Gabriel Mejia, Robin San Roman, Christophe Touret, Corinne Wong, Carleigh Wood, Bokai Yu, Pierre Andrews, Can Balioglu, Peng-Jen Chen, Marta R. Costa-juss{\`a}, Maha Elbayad, Hongyu Gong, Francisco Guzm{\'a}n, Kevin Heffernan, Somya Jain, Justine Kao, Ann Lee, Xutai Ma, Alex Mourachko, Benjamin Peloquin, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Anna Sun, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang, Mary Williamson", journal={ArXiv}, year={2023} } ```
KES/GEC-English	KES	2023-12-03T02:01:30Z	9	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "Guyanese Creole", "Caribbean dialect", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-12T17:56:28Z	--- tags: #- translation - text2text-generation - Guyanese Creole - Caribbean dialect license: apache-2.0 --- # Guyanese English Creole to English Translator This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of Guyanese English Creole to English. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creoles checkout the library [Caribe](https://pypi.org/project/Caribe/). ___ # Usage with Transformers ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KES/GEC-English") model = AutoModelForSeq2SeqLM.from_pretrained("KES/GEC-English") text = "Ah waan ah phone" inputs = tokenizer("guy:"+text, truncation=True, return_tensors='pt') output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True) translation=tokenizer.batch_decode(output, skip_special_tokens=True) print("".join(translation)) #translation: I want a phone. ``` ___
velaa/opt-125m-finetuned-mnli	velaa	2023-12-03T01:53:26Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "opt", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:facebook/opt-125m", "base_model:finetune:facebook/opt-125m", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-11-30T04:15:28Z	--- license: other base_model: facebook/opt-125m tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: opt-125m-finetuned-mnli results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mnli split: validation_matched args: mnli metrics: - name: Accuracy type: accuracy value: 0.35517065715741214 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opt-125m-finetuned-mnli This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.7566 - Accuracy: 0.3552 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0726 \| 1.0 \| 1 \| 1.7835 \| 0.3536 \| \| 0.1157 \| 2.0 \| 2 \| 1.7566 \| 0.3552 \| \| 0.0624 \| 3.0 \| 3 \| 1.7372 \| 0.3548 \| \| 0.07 \| 4.0 \| 4 \| 1.7249 \| 0.3544 \| \| 0.0689 \| 5.0 \| 5 \| 1.7189 \| 0.3545 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
Nachuwu/wav2vec2-fleur-mms-batch6-epoch24-finetunning-3	Nachuwu	2023-12-03T01:27:58Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "base_model:Nachuwu/wav2vec2-fleur-mms-batch6-epoch16", "base_model:finetune:Nachuwu/wav2vec2-fleur-mms-batch6-epoch16", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-03T00:46:15Z	--- license: cc-by-nc-4.0 base_model: Nachuwu/wav2vec2-fleur-mms-batch6-epoch16 tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: wav2vec2-fleur-mms-batch6-epoch24-finetunning-3 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: test+validation args: default metrics: - name: Wer type: wer value: 0.6017069701280228 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-fleur-mms-batch6-epoch24-finetunning-3 This model is a fine-tuned version of [Nachuwu/wav2vec2-fleur-mms-batch6-epoch16](https://huggingface.co/Nachuwu/wav2vec2-fleur-mms-batch6-epoch16) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3000 - Wer: 0.6017 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 6 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 24 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 5.4497 \| 2.38 \| 100 \| 1.6988 \| 0.7297 \| \| 1.3991 \| 4.76 \| 200 \| 1.3838 \| 0.6394 \| \| 1.2352 \| 7.14 \| 300 \| 1.3424 \| 0.6124 \| \| 1.1687 \| 9.52 \| 400 \| 1.3295 \| 0.6138 \| \| 1.1241 \| 11.9 \| 500 \| 1.3126 \| 0.6067 \| \| 1.0908 \| 14.29 \| 600 \| 1.3162 \| 0.6031 \| \| 1.0824 \| 16.67 \| 700 \| 1.3059 \| 0.6017 \| \| 1.0512 \| 19.05 \| 800 \| 1.3027 \| 0.5989 \| \| 1.0258 \| 21.43 \| 900 \| 1.2990 \| 0.6031 \| \| 1.0376 \| 23.81 \| 1000 \| 1.3000 \| 0.6017 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
Pi3141/alpaca-7b-native-enhanced	Pi3141	2023-12-03T01:23:40Z	0	6	adapter-transformers	[ "adapter-transformers", "pytorch", "llama", "text-generation", "en", "license:wtfpl", "region:us" ]	text-generation	2023-12-03T00:17:42Z	--- license: wtfpl language: - en pipeline_tag: text-generation tags: - llama library_name: adapter-transformers --- <p align="center"><i>Original repo: <a href="https://huggingface.co/8bit-coder/alpaca-7b-nativeEnhanced">https://huggingface.co/8bit-coder/alpaca-7b-nativeEnhanced</a><br>This is a fork that restructures files so it can be easily used with <code>git clone</code></i></p> <p align="center"><img src="https://cdn-uploads.huggingface.co/production/uploads/615a1b7a321f65c4da59c3d3/DFHgrYeqJNIchgLrgfZzl.png" height=256></p> <h1 align="center"> Alpaca 7B Native Enhanced </h1> <p align="center">The Most Advanced Alpaca 7B Model</p> ## 📃 Model Facts - Trained natively on 8x Nvidia A100 40GB GPUs; no LoRA used - Trained on the largest & most accurate dataset yet - Enhanced Programming Capabilities - First Alpaca model to have conversational awareness ## 🚀 Quick Start Guide Step 1. Make sure git-lfs is installed and ready to use ([Guide](https://git-lfs.com/)) Step 2. Download and install [text-generation-webui](https://github.com/oobabooga/text-generation-webui) according to the repository's instructions Step 3. Navigate over to one of it's model folders and clone this repository: git clone https://huggingface.co/8bit-coder/alpaca-7b-nativeEnhanced Step 4. Launch the webui, replace "Your name" with "User" and replace the default instruction prompt with: > You are an AI language model designed to assist the User by answering their questions, offering advice, and engaging in casual conversation in a friendly, helpful, and informative manner. You respond clearly, coherently, and you consider the conversation history. > > User: Hey, how's it going? > > Assistant: Hey there! I'm doing great, thank you. What can I help you with today? Let's have a fun chat! Step 5. Change the settings to match this screenshot: ![Settings](https://cdn-uploads.huggingface.co/production/uploads/615a1b7a321f65c4da59c3d3/m8s2o52xN2I6MDy0sZ5rZ.png) ## 📚 Training #### We used 8x Nvidia A100 40GB GPUs for training this model. Training time took ~3 hours and resulting loss was 0.4761 over 3 epochs. The command used for training is as follows > torchrun --nproc_per_node=8 --master_port=3045 ./stanford_alpaca/train.py --model_name_or_path ./llama-7b-hf --data_path ./alpaca-7b-nativeEnhanced/training_files/alpaca-megaset-fixed.json --fp16 True --output_dir ./output_7b --num_train_epochs 3 --per_device_train_batch_size 2 --per_device_eval_batch_size 2 --gradient_accumulation_steps 16 --evaluation_strategy "no" --save_strategy "steps" --save_steps 200 --learning_rate 2e-5 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type "cosine" --logging_steps 1 --fsdp "full_shard auto_wrap" --fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' --tf32 True There's a folder in this repository called training_files. full-training-instructions.txt is the full list of commands from start to finish of training, to converting the model all the way to 4 bit quantized ggml. It is not recommended to quantize this model down to 4 bits. The instructions are included purely for informational purposes. In addition, the training instructions file is built specifically for rented cloud computing. This means that by following the commands in the file, anyone should be able to train a similar model. ### Common errors while training - CUDA Out of Memory error - This is because your GPUs do not have a minimum of 40GB of vram. The weakest GPU that we've been able to successfully train on has been Nvidia A100 40GB. Even with 8 of these, the vram usage was almost always right up at the limit. If you have 40GB GPUs and are still running into this error, try halving the per_device_train_batch_size and per_device_eval_batch_size and doubling the gradient_accumulation_steps. If you have more than 40GB of vram per GPU and wish to train faster, the opposite applies. - LLaMATokenizer error - This happens because you forgot to fix tokenizer_config.json in the llama-7b-hf directory. The fix is to rename LLaMATokenizer to LlamaTokenizer in that file. - RuntimeError: CUDA error: invalid device ordinal - This error occurs when your nproc_per_node is set to a number greater than how many GPUs you have installed in your system. You can check how many GPUs you have installed by running nvidia-smi. - torchrun is not recognized - This error occurs when you have a python version older than 3.10. Follow the instructions in the training instructions file to install miniconda and get python 3.10 set up. Circumventing this error by running python -m torch.distributed.run will not work. Many of the dependencies require python 3.10 and will fatally error out at the start of training. - KeyError - This happens when your JSON training data is broken in some way. Try running the dataset_validator.py in the training_files folder to find the broken key. ## 📝 Notes - The main version of this model is in the hugging face transformers data type. The other one (.pth) format is provided purely for experimental use with llama.cpp and is not guaranteed to have conversational awareness. - This model exhibits weird behavior when quantized to 4 bits. This might be due to the complexity of the model. We recommend the smallest quantization to be 8 bits, but this is untested. - This model is slightly underfitted. We observed that training the model with a smaller gradient accumulation size benefitted the response quality. - This model appears to have full conversational awareness. This means that provided you're running the model in the same configuration we detailed in the Quick Start Guide, you should be able to hold very detailed conversation with the AI without issues. There is a limit to it's memory, and it's 2048 tokens. Beyond that, it'll forget details and will need to be reminded. ## 🔧 Dataset The dataset used for training this model is made from [AlpacaDataCleaned](https://github.com/gururise/AlpacaDataCleaned) and [codealpaca](https://github.com/sahil280114/codealpaca). We combined these datasets for the following reasons: 1. Increased accuracy since the original stanford_alpaca dataset had many errors. 2. Better knowledge in programming 3. More training data We had an issue with the latest AlpacaDataCleaned dataset where at around 90k lines in, one of the keys has a typo. The key is "instruction:" instead of "instruction". We have fixed this error in the provided megaset but if you plan on grabbing directly from AlpacaDataCleaned, make sure to fix this error. Otherwise, the training script will fail due to a KeyError. ## 👨‍💻 Credits Credits go to [Meta](https://github.com/facebookresearch/llama) for creating the foundational LLaMA models and [Stanford](https://github.com/tatsu-lab/stanford_alpaca) for the instructions on how to train. For the dataset, credits go to [AlpacaDataCleaned](https://github.com/gururise/AlpacaDataCleaned) and [codealpaca](https://github.com/sahil280114/codealpaca). Credits also go to [chavinlo](https://huggingface.co/chavinlo/alpaca-native) for creating the original Alpaca 7B Native model, the inspiration behind this model. Lastly, credits go to the homies that stayed up all night again and again: 8bit, π, chug, Taddy, yoyodapro, Symax, and most importantly: stablediffusion for the beautiful artwork
Asheron/SnowballTargetWSL-test1	Asheron	2023-12-03T01:21:32Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-12-03T01:12:06Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Asheron/SnowballTargetWSL-test1 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
LoneStriker/NeuralPivot-Mistral-13B-8.0bpw-h8-exl2	LoneStriker	2023-12-03T00:49:19Z	9	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-03T00:39:48Z	--- license: cc-by-nc-4.0 language: - en pipeline_tag: text-generation --- <p align="center"><font size="7"> <b>Looks like we're in business, boys!</b> </font></p> <p align="center"><img src="https://i.ibb.co/phBm6C9/Screenshot-2023-12-01-211103.png"/> <p align="center"><font size="6"><b><a href="https://iili.io/JzATKe2.png">!!NSFW!! - Erotica Writing Example - !!NSFW!!</font></a></b></p> ### Recipe slices - sources: - - model: maywell/PiVoT-0.1-Starling-LM-RP - layer_range: [0, 24] - sources: - - model: Intel/neural-chat-7b-v3-1 - layer_range: [12, 24] - sources: - - model: maywell/PiVoT-0.1-Starling-LM-RP - layer_range: [8, 32] merge_method: passthrough dtype: bfloat16
LoneStriker/NeuralPivot-Mistral-13B-6.0bpw-h6-exl2	LoneStriker	2023-12-03T00:49:12Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-03T00:19:59Z	--- license: cc-by-nc-4.0 language: - en pipeline_tag: text-generation --- <p align="center"><font size="7"> <b>Looks like we're in business, boys!</b> </font></p> <p align="center"><img src="https://i.ibb.co/phBm6C9/Screenshot-2023-12-01-211103.png"/> <p align="center"><font size="6"><b><a href="https://iili.io/JzATKe2.png">!!NSFW!! - Erotica Writing Example - !!NSFW!!</font></a></b></p> ### Recipe slices - sources: - - model: maywell/PiVoT-0.1-Starling-LM-RP - layer_range: [0, 24] - sources: - - model: Intel/neural-chat-7b-v3-1 - layer_range: [12, 24] - sources: - - model: maywell/PiVoT-0.1-Starling-LM-RP - layer_range: [8, 32] merge_method: passthrough dtype: bfloat16
LoneStriker/NeuralPivot-Mistral-13B-5.0bpw-h6-exl2	LoneStriker	2023-12-03T00:49:06Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-03T00:00:22Z	--- license: cc-by-nc-4.0 language: - en pipeline_tag: text-generation --- <p align="center"><font size="7"> <b>Looks like we're in business, boys!</b> </font></p> <p align="center"><img src="https://i.ibb.co/phBm6C9/Screenshot-2023-12-01-211103.png"/> <p align="center"><font size="6"><b><a href="https://iili.io/JzATKe2.png">!!NSFW!! - Erotica Writing Example - !!NSFW!!</font></a></b></p> ### Recipe slices - sources: - - model: maywell/PiVoT-0.1-Starling-LM-RP - layer_range: [0, 24] - sources: - - model: Intel/neural-chat-7b-v3-1 - layer_range: [12, 24] - sources: - - model: maywell/PiVoT-0.1-Starling-LM-RP - layer_range: [8, 32] merge_method: passthrough dtype: bfloat16
LoneStriker/NeuralPivot-Mistral-13B-3.0bpw-h6-exl2	LoneStriker	2023-12-03T00:48:48Z	24	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T23:24:16Z	--- license: cc-by-nc-4.0 language: - en pipeline_tag: text-generation --- <p align="center"><font size="7"> <b>Looks like we're in business, boys!</b> </font></p> <p align="center"><img src="https://i.ibb.co/phBm6C9/Screenshot-2023-12-01-211103.png"/> <p align="center"><font size="6"><b><a href="https://iili.io/JzATKe2.png">!!NSFW!! - Erotica Writing Example - !!NSFW!!</font></a></b></p> ### Recipe slices - sources: - - model: maywell/PiVoT-0.1-Starling-LM-RP - layer_range: [0, 24] - sources: - - model: Intel/neural-chat-7b-v3-1 - layer_range: [12, 24] - sources: - - model: maywell/PiVoT-0.1-Starling-LM-RP - layer_range: [8, 32] merge_method: passthrough dtype: bfloat16
kaizerBox/RoFormer_small-summarization	kaizerBox	2023-12-03T00:32:00Z	16	0	transformers	[ "transformers", "tensorboard", "safetensors", "roformer", "text-generation", "generated_from_trainer", "dataset:xsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-12-03T00:31:57Z	--- tags: - generated_from_trainer datasets: - xsum model-index: - name: RoFormer_small-summarization results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # RoFormer_small-summarization This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 4.3373 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 4.975 \| 1.0 \| 5762 \| 4.4897 \| \| 4.4149 \| 2.0 \| 11525 \| 4.3647 \| \| 4.3296 \| 3.0 \| 17286 \| 4.3373 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
kerianheYi/CS245-fine-tunedSD13800_14200_14122	kerianheYi	2023-12-03T00:27:35Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dataset:jytjyt05/t_to_m7", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-03T00:16:13Z	--- license: creativeml-openrail-m base_model: kerianheyi/CS245-fine-tunedSD13400_13800_14122 datasets: - jytjyt05/t_to_m7 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - kerianheYi/CS245-fine-tunedSD13800_14200_14122 This pipeline was finetuned from kerianheyi/CS245-fine-tunedSD13400_13800_14122 on the jytjyt05/t_to_m7 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A melSpectrogram for piano solo in Minor']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("kerianheYi/CS245-fine-tunedSD13800_14200_14122", torch_dtype=torch.float16) prompt = "A melSpectrogram for piano solo in Minor" image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 1 * Learning rate: 1e-05 * Batch size: 1 * Gradient accumulation steps: 4 * Image resolution: 512 * Mixed-precision: fp16
nadim365/4412-model-final	nadim365	2023-12-03T00:25:59Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-03T00:24:41Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb model-index: - name: 4412-model-final results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 4412-model-final This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
kerianheYi/CS245-fine-tunedSD13400_13800_14122	kerianheYi	2023-12-03T00:14:43Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dataset:jytjyt05/t_to_m7", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-03T00:03:32Z	--- license: creativeml-openrail-m base_model: kerianheyi/CS245-fine-tunedSD13000_13400_14122 datasets: - jytjyt05/t_to_m7 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - kerianheYi/CS245-fine-tunedSD13400_13800_14122 This pipeline was finetuned from kerianheyi/CS245-fine-tunedSD13000_13400_14122 on the jytjyt05/t_to_m7 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A melSpectrogram for piano solo in Minor']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("kerianheYi/CS245-fine-tunedSD13400_13800_14122", torch_dtype=torch.float16) prompt = "A melSpectrogram for piano solo in Minor" image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 1 * Learning rate: 1e-05 * Batch size: 1 * Gradient accumulation steps: 4 * Image resolution: 512 * Mixed-precision: fp16
ThuyNT03/KLTN_COQE_viT5_OPASL	ThuyNT03	2023-12-03T00:03:31Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-02T22:33:22Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: KLTN_COQE_viT5_OPASL results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # KLTN_COQE_viT5_OPASL This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
pijarcandra22/BartBali2Indo	pijarcandra22	2023-12-02T23:41:16Z	2	0	transformers	[ "transformers", "tf", "bart", "text2text-generation", "generated_from_keras_callback", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-02T19:25:53Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_keras_callback model-index: - name: pijarcandra22/BartBali2Indo results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # pijarcandra22/BartBali2Indo This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0023 - Validation Loss: 2.8624 - Epoch: 56 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.0020 \| 2.8075 \| 0 \| \| 0.0024 \| 2.8006 \| 1 \| \| 0.0027 \| 2.8418 \| 2 \| \| 0.0021 \| 2.8171 \| 3 \| \| 0.0023 \| 2.7964 \| 4 \| \| 0.0027 \| 2.8319 \| 5 \| \| 0.0018 \| 2.8167 \| 6 \| \| 0.0022 \| 2.8269 \| 7 \| \| 0.0021 \| 2.8194 \| 8 \| \| 0.0020 \| 2.8213 \| 9 \| \| 0.0018 \| 2.8459 \| 10 \| \| 0.0022 \| 2.8367 \| 11 \| \| 0.0018 \| 2.7985 \| 12 \| \| 0.0019 \| 2.8249 \| 13 \| \| 0.0026 \| 2.8372 \| 14 \| \| 0.0024 \| 2.8388 \| 15 \| \| 0.0023 \| 2.8350 \| 16 \| \| 0.0023 \| 2.8429 \| 17 \| \| 0.0024 \| 2.7952 \| 18 \| \| 0.0028 \| 2.7758 \| 19 \| \| 0.0025 \| 2.8287 \| 20 \| \| 0.0025 \| 2.8150 \| 21 \| \| 0.0030 \| 2.8394 \| 22 \| \| 0.0019 \| 2.7969 \| 23 \| \| 0.0018 \| 2.8244 \| 24 \| \| 0.0026 \| 2.8472 \| 25 \| \| 0.0017 \| 2.8750 \| 26 \| \| 0.0021 \| 2.8316 \| 27 \| \| 0.0018 \| 2.8080 \| 28 \| \| 0.0018 \| 2.8333 \| 29 \| \| 0.0031 \| 2.8716 \| 30 \| \| 0.0024 \| 2.8551 \| 31 \| \| 0.0027 \| 2.8611 \| 32 \| \| 0.0031 \| 2.8276 \| 33 \| \| 0.0030 \| 2.8264 \| 34 \| \| 0.0025 \| 2.8764 \| 35 \| \| 0.0023 \| 2.8492 \| 36 \| \| 0.0037 \| 2.8445 \| 37 \| \| 0.0024 \| 2.8607 \| 38 \| \| 0.0024 \| 2.8460 \| 39 \| \| 0.0021 \| 2.8844 \| 40 \| \| 0.0031 \| 2.8310 \| 41 \| \| 0.0031 \| 2.8714 \| 42 \| \| 0.0034 \| 2.8768 \| 43 \| \| 0.0028 \| 2.8641 \| 44 \| \| 0.0023 \| 2.8253 \| 45 \| \| 0.0025 \| 2.8205 \| 46 \| \| 0.0024 \| 2.8318 \| 47 \| \| 0.0019 \| 2.8558 \| 48 \| \| 0.0017 \| 2.8302 \| 49 \| \| 0.0017 \| 2.8587 \| 50 \| \| 0.0021 \| 2.8501 \| 51 \| \| 0.0019 \| 2.8433 \| 52 \| \| 0.0017 \| 2.8747 \| 53 \| \| 0.0021 \| 2.8454 \| 54 \| \| 0.0018 \| 2.8685 \| 55 \| \| 0.0023 \| 2.8624 \| 56 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0
pijarcandra22/t5Bali2Indo	pijarcandra22	2023-12-02T23:36:24Z	4	0	transformers	[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-02T15:03:54Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_keras_callback model-index: - name: pijarcandra22/t5Bali2Indo results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # pijarcandra22/t5Bali2Indo This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4589 - Validation Loss: 1.5981 - Epoch: 97 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.5701 \| 1.5227 \| 0 \| \| 0.5700 \| 1.5142 \| 1 \| \| 0.5690 \| 1.5212 \| 2 \| \| 0.5623 \| 1.5221 \| 3 \| \| 0.5686 \| 1.5265 \| 4 \| \| 0.5592 \| 1.5261 \| 5 \| \| 0.5619 \| 1.5208 \| 6 \| \| 0.5615 \| 1.5224 \| 7 \| \| 0.5679 \| 1.5230 \| 8 \| \| 0.5630 \| 1.5250 \| 9 \| \| 0.5621 \| 1.5238 \| 10 \| \| 0.5617 \| 1.5270 \| 11 \| \| 0.5520 \| 1.5271 \| 12 \| \| 0.5530 \| 1.5347 \| 13 \| \| 0.5578 \| 1.5278 \| 14 \| \| 0.5497 \| 1.5280 \| 15 \| \| 0.5513 \| 1.5333 \| 16 \| \| 0.5506 \| 1.5371 \| 17 \| \| 0.5504 \| 1.5337 \| 18 \| \| 0.5499 \| 1.5374 \| 19 \| \| 0.5436 \| 1.5405 \| 20 \| \| 0.5420 \| 1.5382 \| 21 \| \| 0.5462 \| 1.5377 \| 22 \| \| 0.5402 \| 1.5367 \| 23 \| \| 0.5422 \| 1.5345 \| 24 \| \| 0.5408 \| 1.5385 \| 25 \| \| 0.5434 \| 1.5378 \| 26 \| \| 0.5343 \| 1.5381 \| 27 \| \| 0.5368 \| 1.5404 \| 28 \| \| 0.5410 \| 1.5407 \| 29 \| \| 0.5368 \| 1.5417 \| 30 \| \| 0.5344 \| 1.5431 \| 31 \| \| 0.5343 \| 1.5428 \| 32 \| \| 0.5343 \| 1.5454 \| 33 \| \| 0.5300 \| 1.5499 \| 34 \| \| 0.5325 \| 1.5505 \| 35 \| \| 0.5269 \| 1.5427 \| 36 \| \| 0.5217 \| 1.5493 \| 37 \| \| 0.5197 \| 1.5560 \| 38 \| \| 0.5247 \| 1.5520 \| 39 \| \| 0.5200 \| 1.5557 \| 40 \| \| 0.5270 \| 1.5551 \| 41 \| \| 0.5241 \| 1.5518 \| 42 \| \| 0.5163 \| 1.5492 \| 43 \| \| 0.5227 \| 1.5520 \| 44 \| \| 0.5221 \| 1.5552 \| 45 \| \| 0.5123 \| 1.5523 \| 46 \| \| 0.5173 \| 1.5572 \| 47 \| \| 0.5194 \| 1.5571 \| 48 \| \| 0.5159 \| 1.5566 \| 49 \| \| 0.5137 \| 1.5591 \| 50 \| \| 0.5127 \| 1.5533 \| 51 \| \| 0.5094 \| 1.5516 \| 52 \| \| 0.5095 \| 1.5574 \| 53 \| \| 0.5023 \| 1.5609 \| 54 \| \| 0.5040 \| 1.5604 \| 55 \| \| 0.5019 \| 1.5650 \| 56 \| \| 0.5093 \| 1.5577 \| 57 \| \| 0.5050 \| 1.5592 \| 58 \| \| 0.5069 \| 1.5623 \| 59 \| \| 0.4998 \| 1.5635 \| 60 \| \| 0.4936 \| 1.5674 \| 61 \| \| 0.4997 \| 1.5651 \| 62 \| \| 0.4970 \| 1.5648 \| 63 \| \| 0.4927 \| 1.5651 \| 64 \| \| 0.4933 \| 1.5719 \| 65 \| \| 0.4951 \| 1.5699 \| 66 \| \| 0.4963 \| 1.5690 \| 67 \| \| 0.4906 \| 1.5728 \| 68 \| \| 0.4927 \| 1.5740 \| 69 \| \| 0.4884 \| 1.5763 \| 70 \| \| 0.4917 \| 1.5766 \| 71 \| \| 0.4854 \| 1.5740 \| 72 \| \| 0.4793 \| 1.5741 \| 73 \| \| 0.4824 \| 1.5790 \| 74 \| \| 0.4830 \| 1.5760 \| 75 \| \| 0.4842 \| 1.5784 \| 76 \| \| 0.4786 \| 1.5794 \| 77 \| \| 0.4815 \| 1.5733 \| 78 \| \| 0.4791 \| 1.5800 \| 79 \| \| 0.4784 \| 1.5796 \| 80 \| \| 0.4743 \| 1.5835 \| 81 \| \| 0.4766 \| 1.5832 \| 82 \| \| 0.4767 \| 1.5814 \| 83 \| \| 0.4800 \| 1.5832 \| 84 \| \| 0.4787 \| 1.5847 \| 85 \| \| 0.4681 \| 1.5849 \| 86 \| \| 0.4727 \| 1.5875 \| 87 \| \| 0.4716 \| 1.5838 \| 88 \| \| 0.4686 \| 1.5849 \| 89 \| \| 0.4708 \| 1.5851 \| 90 \| \| 0.4697 \| 1.5911 \| 91 \| \| 0.4705 \| 1.5910 \| 92 \| \| 0.4695 \| 1.5934 \| 93 \| \| 0.4670 \| 1.5914 \| 94 \| \| 0.4643 \| 1.5969 \| 95 \| \| 0.4636 \| 1.5945 \| 96 \| \| 0.4589 \| 1.5981 \| 97 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0
kerianheYi/CS245-fine-tunedSD12200_12600_14122	kerianheYi	2023-12-02T23:35:49Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dataset:jytjyt05/t_to_m7", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-02T23:24:28Z	--- license: creativeml-openrail-m base_model: kerianheyi/CS245-fine-tunedSD11800_12200_14122 datasets: - jytjyt05/t_to_m7 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - kerianheYi/CS245-fine-tunedSD12200_12600_14122 This pipeline was finetuned from kerianheyi/CS245-fine-tunedSD11800_12200_14122 on the jytjyt05/t_to_m7 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A melSpectrogram for piano solo in Major']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("kerianheYi/CS245-fine-tunedSD12200_12600_14122", torch_dtype=torch.float16) prompt = "A melSpectrogram for piano solo in Major" image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 1 * Learning rate: 1e-05 * Batch size: 1 * Gradient accumulation steps: 4 * Image resolution: 512 * Mixed-precision: fp16
athirdpath/Thicc-Mistral-19b-FAIL	athirdpath	2023-12-02T23:23:24Z	10	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T12:03:48Z	### What the hell is going on here? I have a theory! But, I have to go to bed, so I'm setting this to upload while I sleep. The 13Bs struggled because they were inherently lopsided. So, with this layout, I not only free up more parameters for further finetuning, I also address the imbalance. Crazy? Maybe. ### Results Unsurprisingly, it is totally demented. It was worth a shot for science's sake, but watching the per-token perplexity and seeing WHERE it fails... I've just come to the conclusion this line of experimentation is indeed a dead end. 7b models are just too small per layer to have the kind of redundancy needed for multiple slices like this, leaving 11b merges as the only real viable enlarged Mistral. Even then, the problems seen here are scaled down but still apparent in 11b, right down to the pattern of what sequences cause massive perplexity spikes. Perhaps, if one toyed with the layer placement just right, you could get a “solid” >7b Mistral merge. Even then, it would be smaller than I really want to work with. 70 billions and merges like Venus and Goliath prove what seems intuitive, higher parameter count models (when executed sanely) will outperform a smaller model at certain tasks. My last foray into this will be a single-join merge that eats a little more into the layers at the beginning and end, hopefully my hypothesis that you can bleed into the last few layers more with Mistral is correct. But multiple joins is a dead-end. ### Recipe slices: - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [0, 25] - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [7, 25] - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [7, 25] - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [7, 32] merge_method: passthrough dtype: bfloat16
athirdpath/Thicc-PianoMaid-19b-FAIL	athirdpath	2023-12-02T23:22:57Z	11	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T12:03:20Z	### What the hell is going on here? I have a theory! But, I have to go to bed, so I'm setting this to upload while I sleep. The 13Bs struggled because they were inherently lopsided. So, with this layout, I not only free up more parameters for further finetuning, I also address the imbalance. Crazy? Maybe.### What the hell is going on here? I have a theory! But, I have to go to bed, so I'm setting this to upload while I sleep. The 13Bs struggled because they were inherently lopsided. So, with this layout, I not only free up more parameters for further finetuning, I also address the imbalance. Crazy? Maybe. ### Results Unsurprisingly, it is totally demented. It was worth a shot for science's sake, but watching the per-token perplexity and seeing WHERE it fails... I've just come to the conclusion this line of experimentation is indeed a dead end. 7b models are just too small per layer to have the kind of redundancy needed for multiple slices like this, leaving 11b merges as the only real viable enlarged Mistral. Even then, the problems seen here are scaled down but still apparent in 11b, right down to the pattern of what sequences cause massive perplexity spikes. Perhaps, if one toyed with the layer placement just right, you could get a “solid” >7b Mistral merge. Even then, it would be smaller than I really want to work with. 70 billions and merges like Venus and Goliath prove what seems intuitive, higher parameter count models (when executed sanely) will outperform a smaller model at certain tasks. My last foray into this will be a single-join merge that eats a little more into the layers at the beginning and end, hopefully my hypothesis that you can bleed into the last few layers more with Mistral is correct. But multiple joins is a dead-end. ### Recipe slices: - sources: - model: chargoddard/loyal-piano-m7 layer_range: [0, 25] - sources: - model: NeverSleep/Noromaid-7b-v0.1.1 layer_range: [7, 25] - sources: - model: chargoddard/loyal-piano-m7 layer_range: [7, 25] - sources: - model: NeverSleep/Noromaid-7b-v0.1.1 layer_range: [7, 32] merge_method: passthrough dtype: bfloat16
athirdpath/PianoMaid-19b-DARE_blended-FAIL	athirdpath	2023-12-02T23:22:31Z	12	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T22:28:02Z	--- license: apache-2.0 --- ### Results Unsurprisingly, it is totally demented. It was worth a shot for science's sake, but watching the per-token perplexity and seeing WHERE it fails... I've just come to the conclusion this line of experimentation is indeed a dead end. 7b models are just too small per layer to have the kind of redundancy needed for multiple slices like this, leaving 11b merges as the only real viable enlarged Mistral. Even then, the problems seen here are scaled down but still apparent in 11b, right down to the pattern of what sequences cause massive perplexity spikes. Perhaps, if one toyed with the layer placement just right, you could get a “solid” >7b Mistral merge. Even then, it would be smaller than I really want to work with. 70 billions and merges like Venus and Goliath prove what seems intuitive, higher parameter count models (when executed sanely) will outperform a smaller model at certain tasks. My last foray into this will be a single-join merge that eats a little more into the layers at the beginning and end, hopefully my hypothesis that you can bleed into the last few layers more with Mistral is correct. But multiple joins is a dead-end.
azianmike3/mchlsbl	azianmike3	2023-12-02T23:18:51Z	1	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-02T23:15:06Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### mchlsbl Dreambooth model trained by azianmike3 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
Shijia/ClinicalT5-base-finetuned-biomedical	Shijia	2023-12-02T23:17:10Z	272	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:sem_eval_2024_task_2", "base_model:luqh/ClinicalT5-base", "base_model:finetune:luqh/ClinicalT5-base", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-02T21:25:12Z	--- base_model: luqh/ClinicalT5-base tags: - generated_from_trainer datasets: - sem_eval_2024_task_2 metrics: - rouge model-index: - name: ClinicalT5-base-finetuned-biomedical results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: sem_eval_2024_task_2 type: sem_eval_2024_task_2 config: sem_eval_2024_task_2_source split: validation args: sem_eval_2024_task_2_source metrics: - name: Rouge1 type: rouge value: 51.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ClinicalT5-base-finetuned-biomedical This model is a fine-tuned version of [luqh/ClinicalT5-base](https://huggingface.co/luqh/ClinicalT5-base) on the sem_eval_2024_task_2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2017 - Rouge1: 51.0 - Rouge2: 0.0 - Rougel: 51.0 - Rougelsum: 51.0 - Gen Len: 3.71 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 425 \| 0.2227 \| 49.5 \| 0.0 \| 49.5 \| 49.5 \| 3.015 \| \| 1.7568 \| 2.0 \| 850 \| 0.2053 \| 49.0 \| 0.0 \| 49.0 \| 49.0 \| 3.09 \| \| 0.227 \| 3.0 \| 1275 \| 0.2012 \| 51.0 \| 0.0 \| 51.0 \| 51.0 \| 3.24 \| \| 0.2186 \| 4.0 \| 1700 \| 0.2011 \| 52.0 \| 0.0 \| 52.0 \| 52.0 \| 3.29 \| \| 0.2173 \| 5.0 \| 2125 \| 0.2017 \| 51.0 \| 0.0 \| 51.0 \| 51.0 \| 3.71 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
TheBloke/DiscoLM-120b-AWQ	TheBloke	2023-12-02T23:14:26Z	13	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "goliath", "deutsch", "llama2", "discoresearch", "en", "dataset:Open-Orca/SlimOrca-Dedup", "dataset:teknium/openhermes", "dataset:meta-math/MetaMathQA", "dataset:migtissera/Synthia-v1.3", "dataset:THUDM/AgentInstruct", "dataset:LeoLM/German_Songs", "dataset:LeoLM/German_Poems", "dataset:LeoLM/OpenSchnabeltier", "dataset:bjoernp/ultrachat_de", "base_model:DiscoResearch/DiscoLM-120b", "base_model:quantized:DiscoResearch/DiscoLM-120b", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]	text-generation	2023-12-02T15:09:57Z	--- base_model: DiscoResearch/DiscoLM-120b datasets: - Open-Orca/SlimOrca-Dedup - teknium/openhermes - meta-math/MetaMathQA - migtissera/Synthia-v1.3 - THUDM/AgentInstruct - LeoLM/German_Songs - LeoLM/German_Poems - LeoLM/OpenSchnabeltier - bjoernp/ultrachat_de inference: false language: - en library_name: transformers license: llama2 model_creator: Disco Research model_name: DiscoLM 120B model_type: llama pipeline_tag: text-generation prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke tags: - goliath - deutsch - llama2 - discoresearch --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # DiscoLM 120B - AWQ - Model creator: [Disco Research](https://huggingface.co/DiscoResearch) - Original model: [DiscoLM 120B](https://huggingface.co/DiscoResearch/DiscoLM-120b) <!-- description start --> ## Description This repo contains AWQ model files for [Disco Research's DiscoLM 120B](https://huggingface.co/DiscoResearch/DiscoLM-120b). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/DiscoLM-120b-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/DiscoLM-120b-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/DiscoLM-120b-GGUF) * [Disco Research's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/DiscoResearch/DiscoLM-120b) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. \| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \| \| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/DiscoLM-120b-AWQ/tree/main) \| 4 \| 128 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 61.96 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/DiscoLM-120b-AWQ`. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `DiscoLM-120b-AWQ` 7. Select Loader: AutoAWQ. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. 10. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/DiscoLM-120b-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/DiscoLM-120b-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/DiscoLM-120b-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/DiscoLM-120b-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, *generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Disco Research's DiscoLM 120B ![EM Logo](https://raw.githubusercontent.com/jphme/jpdus.github.io/master/images/discoresearch.webp) # DiscoLM 120b (Alpha) DiscoLM 120b (Alpha) is an experimental 120b model based on [Alpindale´s Goliath 120b](https://huggingface.co/alpindale/goliath-120b), a merge of different Llama2-70b models, and further finetuned on a dataset of some the most popular open-source instruction sets. Disco 120b is a [DiscoResearch](https://huggingface.co/DiscoResearch) project and was trained by [Björn Plüster](https://huggingface.co/bjoernp). The model was trained with compute provided by [HessianAI](https://hessian.ai/) - we are very grateful for their support; please check out their wesbite and projects! <img src="https://hessian.ai/wp-content/themes/hessianai/img/hessian-ai-logo.svg" width="120"> ## Table of Contents 1. [Download](#download) 2. [Benchmarks](#benchmarks) 3. [Prompt Format](#prompt-format) 4. [Dataset](#dataset) 5. [Acknowledgements](#acknowledgements) 6. [Contact](#contact) 7. [About DiscoResearch](#about-discoresearch) 8. [Disclaimer](#disclaimer) ## Download \| Huggingface \| GPTQ \| GGUF \| AWQ \| Base Model \| \|-------\|-------\|-------\|-------\|-------\| \| [Link](https://huggingface.co/DiscoResearch/DiscoLM-120b) \| soon \| soon \| soon \| [Goliath 120b](https://huggingface.co/alpindale/goliath-120b) \| ## Benchmarks ### Hugginface Leaderboard This models is still an early Alpha and we can't guarantee that there isn't any contamination. However, the average of 72.15 would earn the #2 spot on the HF leaderboard at the time of writing and the highest score for a >70b model yet. \| Metric \| Value \| \|-----------------------\|-------\| \| ARC (25-shot) \| 69.54 \| \| HellaSwag (10-shot) \| 86.49 \| \| MMLU (5-shot) \| 70.32 \| \| TruthfulQA (0-shot) \| 61.42 \| \| Winogrande (5-shot) \| 83.03 \| \| GSM8k (5-shot) \| 68.39 \| \| Avg. \| 72.15 \| We use [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. ### FastEval \| Metric \| Value \| \|-----------------------\|-------\| \| GSM8K \| 81.2 \| \| Math \| 22.3 \| \| BBH \| 72.9 \| \| MMLU \| 67.9 \| \| Avg. \| 53.3 \| ### MTBench ```json { "first_turn": 8.45, "second_turn": 7.45, "categories": { "writing": 9.4, "roleplay": 8.65, "reasoning": 6.85, "math": 5.55, "coding": 4.95, "extraction": 9.15, "stem": 9.225, "humanities": 9.825 }, "average": 7.95 } ``` ## Prompt Format This model follows the ChatML format: ``` <\|im_start\|>system You are DiscoLM, a helpful assistant. <\|im_end\|> <\|im_start\|>user Please tell me possible reasons to call a research collective "Disco Research"<\|im_end\|> <\|im_start\|>assistant ``` This formatting is also available via a pre-defined Transformers chat template, which means that lists of messages can be formatted for you with the apply_chat_template() method: ```python chat = [ {"role": "system", "content": "You are DiscoLM, a helpful assistant."}, {"role": "user", "content": "Please tell me possible reasons to call a research collective Disco Research"} ] tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` If you use `tokenize=True` and `return_tensors="pt"` instead, then you will get a tokenized and formatted conversation ready to pass to `model.generate()`. ## Dataset The dataset curation for DiscoLM 120b followed a "brute force"/"PoC" approach, as one goal was to see whether a 120b model can "absorb" more instruction data than a 70b model. The following datasets were used for training DiscoLM 120b: * [SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup) * [OpenPlatypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) * [OpenHermes](https://huggingface.co/datasets/teknium/openhermes) * [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) * [UltraChat](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) * [Synthia v.1.3](https://huggingface.co/datasets/migtissera/Synthia-v1.3) * [AgentInstruct](https://huggingface.co/datasets/THUDM/AgentInstruct) Many thanks for all dataset providers/curators! ## Contact Best way to reach us is on our [Discord](https://discord.gg/4pAqJP7W). ## About DiscoResearch DiscoResearch is an aspiring open research community. Disco should be a place where researchers from many communities can come together to combine their expertise and create innovative and groundbreaking LLMs. Come join our Discord, share your opinions and ideas, and advance open LLM research with us! ## Acknowledgements Disco 120b is a [DiscoResearch](https://huggingface.co/DiscoResearch) project and was trained by [Björn Plüster](https://huggingface.co/bjoernp). [Jan Harries](https://huggingface.co/jphme) helped with technical adivce, logistics and the Model Card and [AutoMeta](https://huggingface.co/Alignment-Lab-AI) also provided helpful technical adivce. The model was trained with compute provided by [HessianAI](https://hessian.ai/) - many thanks in particular to [Patrick Schramowski](https://huggingface.co/PSaiml) for his support. We are standing on the shoulders of giants; many thanks in no particular order to [alpindale](https://huggingface.co/alpindale) for Goliath 120b (with important contributions by [Charles Goddard](https://huggingface.co/chargoddard) and [Undi95](https://huggingface.co/Undi95)), [TheBloke](https://huggingface.co/TheBloke) for providing quantized versions, [winglian](https://huggingface.co/winglian) for Axolotl which was used to train the model and the SlimOrca dataset, [garage-bAInd](https://huggingface.co/garage-bAInd), [Teknium](https://huggingface.co/teknium), [Migel Tissera](https://huggingface.co/migtissera), [MetaMath](https://huggingface.co/meta-math) for their great datasets (please contact us if we forgot to mention you here!). [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. This model should only be used for research purposes. The original Llama2 license and all restrictions of datasets used to train this model apply.
kerianheYi/CS245-fine-tunedSD11400_11800_14122	kerianheYi	2023-12-02T23:07:06Z	3	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dataset:jytjyt05/t_to_m7", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-02T22:44:08Z	--- license: creativeml-openrail-m base_model: kerianheyi/CS245-fine-tunedSD11000_11400_14122 datasets: - jytjyt05/t_to_m7 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - kerianheYi/CS245-fine-tunedSD11400_11800_14122 This pipeline was finetuned from kerianheyi/CS245-fine-tunedSD11000_11400_14122 on the jytjyt05/t_to_m7 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A melSpectrogram for piano solo in Major']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("kerianheYi/CS245-fine-tunedSD11400_11800_14122", torch_dtype=torch.float16) prompt = "A melSpectrogram for piano solo in Major" image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 1 * Learning rate: 1e-05 * Batch size: 1 * Gradient accumulation steps: 4 * Image resolution: 512 * Mixed-precision: fp16
qwebeck/cartople-reinforce	qwebeck	2023-12-02T23:01:11Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-12-02T23:00:30Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: cartople-reinforce results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 495.23 +/- 44.27 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
SuperMaker/vit-base-patch16-224-in21k-leukemia	SuperMaker	2023-12-02T22:42:35Z	11	0	transformers	[ "transformers", "tf", "tensorboard", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-02T16:08:05Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: vit-base-patch16-224-in21k-leukemia results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-leukemia This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Leukemia Dataset hosted on kaggle https://www.kaggle.com/datasets/andrewmvd/leukemia-classification. It achieves the following results on the evaluation set: - Train Loss: 0.3256 - Train Accuracy: 0.8795 - Validation Loss: 0.6907 - Validation Accuracy: 0.6848 - Epoch: 13 ## Model description Google Vision Transormer (ViT). fine-tuned on the white blood cancer - Leukemia - dataset ## Intended uses & limitations This model was fine-tuned as a part of my project `LeukemiaAI`, a fully integrated pipeline to detect Leukemia. Github Repo: https://github.com/MohammedSaLah-Eldeen/LeukemiaAI ### Training hyperparameters - training_precision: mixed_float16 - optimizer: { 'inner_optimizer': { 'module': 'keras.optimizers.experimental', 'class_name': 'SGD', 'config': { 'name': 'SGD', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': 1, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': { 'module': 'keras.optimizers.schedules', 'class_name': 'CosineDecay', 'config': { 'initial_learning_rate': 0.001, 'decay_steps': 896, 'alpha': 0.0, 'name': None, 'warmup_target': None, 'warmup_steps': 0 }, 'registered_name': None }, 'momentum': 0.9, 'nesterov': False }, 'registered_name': None }, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000 } ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.5007 \| 0.7629 \| 0.7206 \| 0.6643 \| 0 \| \| 0.3958 \| 0.8418 \| 0.7137 \| 0.6686 \| 1 \| \| 0.3578 \| 0.8632 \| 0.6998 \| 0.6789 \| 2 \| \| 0.3377 \| 0.8713 \| 0.6899 \| 0.6843 \| 3 \| \| 0.3274 \| 0.8778 \| 0.6869 \| 0.6832 \| 4 \| \| 0.3261 \| 0.8792 \| 0.6880 \| 0.6859 \| 5 \| \| 0.3257 \| 0.8797 \| 0.6906 \| 0.6848 \| 6 \| \| 0.3255 \| 0.8796 \| 0.6896 \| 0.6859 \| 7 \| \| 0.3256 \| 0.8794 \| 0.6901 \| 0.6848 \| 8 \| \| 0.3258 \| 0.8795 \| 0.6867 \| 0.6864 \| 9 \| \| 0.3258 \| 0.8793 \| 0.6896 \| 0.6859 \| 10 \| \| 0.3256 \| 0.8796 \| 0.6871 \| 0.6864 \| 11 \| \| 0.3255 \| 0.8795 \| 0.6897 \| 0.6853 \| 12 \| \| 0.3256 \| 0.8795 \| 0.6907 \| 0.6848 \| 13 \| ### Framework versions - Transformers 4.35.0 - TensorFlow 2.13.0 - Datasets 2.1.0 - Tokenizers 0.14.1
simonveitner/MathHermes-2.5-Mistral-7B	simonveitner	2023-12-02T22:35:30Z	56	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "distillation", "dpo", "rlhf", "conversational", "en", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:finetune:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T22:13:47Z	--- base_model: teknium/OpenHermes-2.5-Mistral-7B tags: - mistral - instruct - finetune - chatml - gpt4 - synthetic data - distillation - dpo - rlhf license: apache-2.0 language: - en dataset: argilla/distilabel-math-preference-dpo --- This model was finetuned with DPO technique. The goal was to experiment if the base models capabilities in mathematics can be increased. ## From the original model card: # Prompt Format OpenHermes 2.5 now uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts are now a thing that matters! Hermes 2.5 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by a man named Teknium, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out.
Hanzalwi/XGLM-564M-finetuned-aings-validation-data-1	Hanzalwi	2023-12-02T22:28:09Z	11	0	peft	[ "peft", "tensorboard", "safetensors", "xglm", "arxiv:1910.09700", "base_model:facebook/xglm-564M", "base_model:adapter:facebook/xglm-564M", "region:us" ]	null	2023-12-02T14:56:30Z	--- library_name: peft base_model: facebook/xglm-564M --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.3.dev0
Mathews/huggingcartoon	Mathews	2023-12-02T22:26:48Z	1	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-02T22:22:46Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### HuggingCartoon Dreambooth model trained by Mathews with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
kaokao01/ppo-Huggy	kaokao01	2023-12-02T22:25:13Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-12-02T22:25:07Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: kaokao01/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
vicfeuga/ppo-SoccerTwos	vicfeuga	2023-12-02T22:17:35Z	0	0	ml-agents	[ "ml-agents", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-12-02T22:17:35Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: vicfeuga/ppo-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
linqus/ppo-Huggy	linqus	2023-12-02T22:13:33Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-12-02T22:13:27Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: linqus/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
kerianheYi/CS245-fine-tunedSD10600_11000_14122	kerianheYi	2023-12-02T22:07:39Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dataset:jytjyt05/t_to_m7", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-02T21:56:31Z	--- license: creativeml-openrail-m base_model: kerianheyi/CS245-fine-tunedSD10200_10600_14122 datasets: - jytjyt05/t_to_m7 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - kerianheYi/CS245-fine-tunedSD10600_11000_14122 This pipeline was finetuned from kerianheyi/CS245-fine-tunedSD10200_10600_14122 on the jytjyt05/t_to_m7 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A melSpectrogram for piano solo in Major']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("kerianheYi/CS245-fine-tunedSD10600_11000_14122", torch_dtype=torch.float16) prompt = "A melSpectrogram for piano solo in Major" image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 1 * Learning rate: 1e-05 * Batch size: 1 * Gradient accumulation steps: 4 * Image resolution: 512 * Mixed-precision: fp16
Yunas2002/finetuning-sentiment-model-3000-samples	Yunas2002	2023-12-02T21:49:57Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-02T19:50:44Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8666666666666667 - name: F1 type: f1 value: 0.870967741935484 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3490 - Accuracy: 0.8667 - F1: 0.8710 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
annabellehuether/bert-base-cased-supreme-court-summaries-32batch	annabellehuether	2023-12-02T21:37:16Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-02T21:05:07Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-cased-supreme-court-summaries-32batch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-supreme-court-summaries-32batch This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6697 - Accuracy: 0.6241 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 7 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.636 \| 1.0 \| 660 \| 0.6283 \| 0.6285 \| \| 0.6005 \| 2.0 \| 1320 \| 0.6228 \| 0.6333 \| \| 0.561 \| 3.0 \| 1980 \| 0.6697 \| 0.6241 \| ### Framework versions - Transformers 4.35.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
yassinox/ppo-Huggy	yassinox	2023-12-02T21:31:46Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-12-02T21:31:34Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: yassinox/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
LoneStriker/DPOpenHermes-7B-8.0bpw-h8-exl2	LoneStriker	2023-12-02T21:23:40Z	9	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:teknium/openhermes", "dataset:argilla/ultrafeedback-binarized-preferences", "dataset:Intel/orca_dpo_pairs", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:finetune:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T21:19:12Z	--- base_model: teknium/OpenHermes-2.5-Mistral-7B license: apache-2.0 datasets: - teknium/openhermes - argilla/ultrafeedback-binarized-preferences - Intel/orca_dpo_pairs language: - en library_name: transformers pipeline_tag: text-generation --- # DPOpenHermes 7B ![image/png](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B/resolve/main/assets/dpopenhermes.png) ## OpenHermes x Notus x Neural This is an RL fine tuned model of [Teknium](https://huggingface.co/teknium)'s [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) using the [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) and [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) preference datasets for reinforcement learning using Direct Preference Optimization (DPO) DPOpenHermes is trained using qLoRA. The adapter is also provided in this model repo. # Training Details [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) DPOpenHermes was trained on a single H100 80GB hosted on RunPod for ~10h for 0.6 epochs of the dataset. https://wandb.ai/oaaic/openhermes-dpo/reports/DPOpenHermes--Vmlldzo2MTQ3NDg2 # Prompt Format DPOpenHermes uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts are now a thing that matters! Hermes 2.5 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by a man named Teknium, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. Currently, I recommend using LM Studio for chatting with Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) # Benchmarks ## AGIEval ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------\|------:\|--------\|-----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|0.2480\|_ \|0.0272\| \| \| \|acc_norm\|0.2520\|_ \|0.0273\| \|agieval_logiqa_en \| 0\|acc \|0.3810\|_ \|0.0190\| \| \| \|acc_norm\|0.3856\|_ \|0.0191\| \|agieval_lsat_ar \| 0\|acc \|0.2348\|_ \|0.0280\| \| \| \|acc_norm\|0.2304\|_ \|0.0278\| \|agieval_lsat_lr \| 0\|acc \|0.5118\|_ \|0.0222\| \| \| \|acc_norm\|0.5196\|_ \|0.0221\| \|agieval_lsat_rc \| 0\|acc \|0.5948\|_ \|0.0300\| \| \| \|acc_norm\|0.5688\|_ \|0.0303\| \|agieval_sat_en \| 0\|acc \|0.7427\|_ \|0.0305\| \| \| \|acc_norm\|0.7427\|_ \|0.0305\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4563\|_ \|0.0348\| \| \| \|acc_norm\|0.4515\|_ \|0.0348\| \|agieval_sat_math \| 0\|acc \|0.3818\|_ \|0.0328\| \| \| \|acc_norm\|0.3682\|_ \|0.0326\| ``` Average: 0.4399 ## GPT4All ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5930\|_ \|0.0144\| \| \| \|acc_norm\|0.6323\|_ \|0.0141\| \|arc_easy \| 0\|acc \|0.8443\|_ \|0.0074\| \| \| \|acc_norm\|0.8295\|_ \|0.0077\| \|boolq \| 1\|acc \|0.8599\|_ \|0.0061\| \|hellaswag \| 0\|acc \|0.6548\|_ \|0.0047\| \| \| \|acc_norm\|0.8365\|_ \|0.0037\| \|openbookqa \| 0\|acc \|0.3520\|_ \|0.0214\| \| \| \|acc_norm\|0.4640\|_ \|0.0223\| \|piqa \| 0\|acc \|0.8210\|_ \|0.0089\| \| \| \|acc_norm\|0.8335\|_ \|0.0087\| \|winogrande \| 0\|acc \|0.7466\|_ \|0.0122\| ``` Average: 0.7431 ## TruthfulQA ``` hf-causal-experimental (pretrained=openaccess-ai-collective/dpopenhermes-alpha-v1,dtype=bfloat16,trust_remote_code=True,use_accelerate=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16 \| Task \|Version\|Metric\|Value \| \|Stderr\| \|-------------\|------:\|------\|-----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|0.4186\|_ \|0.0173\| \| \| \|mc2 \|0.5847\|_ \|0.0153\| ```
1treu1/ppo-Huggy	1treu1	2023-12-02T21:22:32Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-12-02T21:22:24Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: 1treu1/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
LoneStriker/DPOpenHermes-7B-6.0bpw-h6-exl2	LoneStriker	2023-12-02T21:12:42Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:teknium/openhermes", "dataset:argilla/ultrafeedback-binarized-preferences", "dataset:Intel/orca_dpo_pairs", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:finetune:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T21:09:15Z	--- base_model: teknium/OpenHermes-2.5-Mistral-7B license: apache-2.0 datasets: - teknium/openhermes - argilla/ultrafeedback-binarized-preferences - Intel/orca_dpo_pairs language: - en library_name: transformers pipeline_tag: text-generation --- # DPOpenHermes 7B ![image/png](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B/resolve/main/assets/dpopenhermes.png) ## OpenHermes x Notus x Neural This is an RL fine tuned model of [Teknium](https://huggingface.co/teknium)'s [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) using the [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) and [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) preference datasets for reinforcement learning using Direct Preference Optimization (DPO) DPOpenHermes is trained using qLoRA. The adapter is also provided in this model repo. # Training Details [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) DPOpenHermes was trained on a single H100 80GB hosted on RunPod for ~10h for 0.6 epochs of the dataset. https://wandb.ai/oaaic/openhermes-dpo/reports/DPOpenHermes--Vmlldzo2MTQ3NDg2 # Prompt Format DPOpenHermes uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts are now a thing that matters! Hermes 2.5 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by a man named Teknium, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. Currently, I recommend using LM Studio for chatting with Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) # Benchmarks ## AGIEval ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------\|------:\|--------\|-----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|0.2480\|_ \|0.0272\| \| \| \|acc_norm\|0.2520\|_ \|0.0273\| \|agieval_logiqa_en \| 0\|acc \|0.3810\|_ \|0.0190\| \| \| \|acc_norm\|0.3856\|_ \|0.0191\| \|agieval_lsat_ar \| 0\|acc \|0.2348\|_ \|0.0280\| \| \| \|acc_norm\|0.2304\|_ \|0.0278\| \|agieval_lsat_lr \| 0\|acc \|0.5118\|_ \|0.0222\| \| \| \|acc_norm\|0.5196\|_ \|0.0221\| \|agieval_lsat_rc \| 0\|acc \|0.5948\|_ \|0.0300\| \| \| \|acc_norm\|0.5688\|_ \|0.0303\| \|agieval_sat_en \| 0\|acc \|0.7427\|_ \|0.0305\| \| \| \|acc_norm\|0.7427\|_ \|0.0305\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4563\|_ \|0.0348\| \| \| \|acc_norm\|0.4515\|_ \|0.0348\| \|agieval_sat_math \| 0\|acc \|0.3818\|_ \|0.0328\| \| \| \|acc_norm\|0.3682\|_ \|0.0326\| ``` Average: 0.4399 ## GPT4All ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5930\|_ \|0.0144\| \| \| \|acc_norm\|0.6323\|_ \|0.0141\| \|arc_easy \| 0\|acc \|0.8443\|_ \|0.0074\| \| \| \|acc_norm\|0.8295\|_ \|0.0077\| \|boolq \| 1\|acc \|0.8599\|_ \|0.0061\| \|hellaswag \| 0\|acc \|0.6548\|_ \|0.0047\| \| \| \|acc_norm\|0.8365\|_ \|0.0037\| \|openbookqa \| 0\|acc \|0.3520\|_ \|0.0214\| \| \| \|acc_norm\|0.4640\|_ \|0.0223\| \|piqa \| 0\|acc \|0.8210\|_ \|0.0089\| \| \| \|acc_norm\|0.8335\|_ \|0.0087\| \|winogrande \| 0\|acc \|0.7466\|_ \|0.0122\| ``` Average: 0.7431 ## TruthfulQA ``` hf-causal-experimental (pretrained=openaccess-ai-collective/dpopenhermes-alpha-v1,dtype=bfloat16,trust_remote_code=True,use_accelerate=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16 \| Task \|Version\|Metric\|Value \| \|Stderr\| \|-------------\|------:\|------\|-----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|0.4186\|_ \|0.0173\| \| \| \|mc2 \|0.5847\|_ \|0.0153\| ```
kaizerBox/RoFormer-summarization	kaizerBox	2023-12-02T21:09:04Z	21	0	transformers	[ "transformers", "tensorboard", "safetensors", "roformer", "text-generation", "generated_from_trainer", "dataset:xsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T21:09:01Z	--- tags: - generated_from_trainer datasets: - xsum model-index: - name: RoFormer-summarization results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # RoFormer-summarization This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 3.8763 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 4.5465 \| 1.0 \| 5762 \| 4.0642 \| \| 3.9616 \| 2.0 \| 11525 \| 3.9113 \| \| 3.8473 \| 3.0 \| 17286 \| 3.8763 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
gmmarcos/dqn-SpaceInvadersNoFrameskip-v4	gmmarcos	2023-12-02T21:07:49Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-02T21:07:11Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 618.00 +/- 84.59 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga gmmarcos -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga gmmarcos -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga gmmarcos ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
canlinzhang/wav2vec2_speech_emotion_recognition_trained_on_IEMOCAP	canlinzhang	2023-12-02T21:03:37Z	49	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "endpoints_compatible", "region:us" ]	audio-classification	2023-03-10T03:49:54Z	This model is fine tuned on the IEMOCAP dataset. We applied volume normalization and data augmentation (noise injection, pitch shift and audio stretching). Also, this is a speaker independent model: We use Ses05F in the IEMOCAP dataset as validation speaker and Ses05M as test speaker. The initial pre-trained model is facebook/wav2vec2-base. The fine tune dataset only contains 4 common emotions of IEMOCAP (happy, angry, sad, neutral), without frustration. The audios are either padded or trimed to 8-sec-long before fine tuning. After 10 epoches of training, the validation accuracy is around 67%. In order to impliment this model: Please run the following code in a python script: ``` from transformers import AutoFeatureExtractor, AutoModelForAudioClassification import librosa import torch target_sampling_rate = 16000 model_name = 'canlinzhang/wav2vec2_speech_emotion_recognition_trained_on_IEMOCAP' audio_path = your_audio_path #build id and label dicts id2label = {0:'neu', 1:'ang', 2:'sad', 3:'hap'} label2id = {'neu':0, 'ang':1, 'sad':2, 'hap':3} feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) model = AutoModelForAudioClassification.from_pretrained(model_name) y_ini, sr_ini = librosa.load(audio_path, sr=target_sampling_rate) inputs = feature_extractor(y_ini, sampling_rate=target_sampling_rate, return_tensors="pt") logits = model(**inputs).logits predicted_class_ids = torch.argmax(logits).item() pred_class = id2label[predicted_class_ids] print(pred_class) ```
LoneStriker/DPOpenHermes-7B-4.0bpw-h6-exl2	LoneStriker	2023-12-02T20:52:08Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:teknium/openhermes", "dataset:argilla/ultrafeedback-binarized-preferences", "dataset:Intel/orca_dpo_pairs", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:finetune:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T20:49:44Z	--- base_model: teknium/OpenHermes-2.5-Mistral-7B license: apache-2.0 datasets: - teknium/openhermes - argilla/ultrafeedback-binarized-preferences - Intel/orca_dpo_pairs language: - en library_name: transformers pipeline_tag: text-generation --- # DPOpenHermes 7B ![image/png](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B/resolve/main/assets/dpopenhermes.png) ## OpenHermes x Notus x Neural This is an RL fine tuned model of [Teknium](https://huggingface.co/teknium)'s [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) using the [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) and [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) preference datasets for reinforcement learning using Direct Preference Optimization (DPO) DPOpenHermes is trained using qLoRA. The adapter is also provided in this model repo. # Training Details [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) DPOpenHermes was trained on a single H100 80GB hosted on RunPod for ~10h for 0.6 epochs of the dataset. https://wandb.ai/oaaic/openhermes-dpo/reports/DPOpenHermes--Vmlldzo2MTQ3NDg2 # Prompt Format DPOpenHermes uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts are now a thing that matters! Hermes 2.5 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by a man named Teknium, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. Currently, I recommend using LM Studio for chatting with Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) # Benchmarks ## AGIEval ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------\|------:\|--------\|-----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|0.2480\|_ \|0.0272\| \| \| \|acc_norm\|0.2520\|_ \|0.0273\| \|agieval_logiqa_en \| 0\|acc \|0.3810\|_ \|0.0190\| \| \| \|acc_norm\|0.3856\|_ \|0.0191\| \|agieval_lsat_ar \| 0\|acc \|0.2348\|_ \|0.0280\| \| \| \|acc_norm\|0.2304\|_ \|0.0278\| \|agieval_lsat_lr \| 0\|acc \|0.5118\|_ \|0.0222\| \| \| \|acc_norm\|0.5196\|_ \|0.0221\| \|agieval_lsat_rc \| 0\|acc \|0.5948\|_ \|0.0300\| \| \| \|acc_norm\|0.5688\|_ \|0.0303\| \|agieval_sat_en \| 0\|acc \|0.7427\|_ \|0.0305\| \| \| \|acc_norm\|0.7427\|_ \|0.0305\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4563\|_ \|0.0348\| \| \| \|acc_norm\|0.4515\|_ \|0.0348\| \|agieval_sat_math \| 0\|acc \|0.3818\|_ \|0.0328\| \| \| \|acc_norm\|0.3682\|_ \|0.0326\| ``` Average: 0.4399 ## GPT4All ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5930\|_ \|0.0144\| \| \| \|acc_norm\|0.6323\|_ \|0.0141\| \|arc_easy \| 0\|acc \|0.8443\|_ \|0.0074\| \| \| \|acc_norm\|0.8295\|_ \|0.0077\| \|boolq \| 1\|acc \|0.8599\|_ \|0.0061\| \|hellaswag \| 0\|acc \|0.6548\|_ \|0.0047\| \| \| \|acc_norm\|0.8365\|_ \|0.0037\| \|openbookqa \| 0\|acc \|0.3520\|_ \|0.0214\| \| \| \|acc_norm\|0.4640\|_ \|0.0223\| \|piqa \| 0\|acc \|0.8210\|_ \|0.0089\| \| \| \|acc_norm\|0.8335\|_ \|0.0087\| \|winogrande \| 0\|acc \|0.7466\|_ \|0.0122\| ``` Average: 0.7431 ## TruthfulQA ``` hf-causal-experimental (pretrained=openaccess-ai-collective/dpopenhermes-alpha-v1,dtype=bfloat16,trust_remote_code=True,use_accelerate=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16 \| Task \|Version\|Metric\|Value \| \|Stderr\| \|-------------\|------:\|------\|-----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|0.4186\|_ \|0.0173\| \| \| \|mc2 \|0.5847\|_ \|0.0153\| ```
LoneStriker/DPOpenHermes-7B-3.0bpw-h6-exl2	LoneStriker	2023-12-02T20:41:54Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:teknium/openhermes", "dataset:argilla/ultrafeedback-binarized-preferences", "dataset:Intel/orca_dpo_pairs", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:finetune:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T20:40:00Z	--- base_model: teknium/OpenHermes-2.5-Mistral-7B license: apache-2.0 datasets: - teknium/openhermes - argilla/ultrafeedback-binarized-preferences - Intel/orca_dpo_pairs language: - en library_name: transformers pipeline_tag: text-generation --- # DPOpenHermes 7B ![image/png](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B/resolve/main/assets/dpopenhermes.png) ## OpenHermes x Notus x Neural This is an RL fine tuned model of [Teknium](https://huggingface.co/teknium)'s [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) using the [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) and [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) preference datasets for reinforcement learning using Direct Preference Optimization (DPO) DPOpenHermes is trained using qLoRA. The adapter is also provided in this model repo. # Training Details [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) DPOpenHermes was trained on a single H100 80GB hosted on RunPod for ~10h for 0.6 epochs of the dataset. https://wandb.ai/oaaic/openhermes-dpo/reports/DPOpenHermes--Vmlldzo2MTQ3NDg2 # Prompt Format DPOpenHermes uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts are now a thing that matters! Hermes 2.5 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by a man named Teknium, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. Currently, I recommend using LM Studio for chatting with Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) # Benchmarks ## AGIEval ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------\|------:\|--------\|-----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|0.2480\|_ \|0.0272\| \| \| \|acc_norm\|0.2520\|_ \|0.0273\| \|agieval_logiqa_en \| 0\|acc \|0.3810\|_ \|0.0190\| \| \| \|acc_norm\|0.3856\|_ \|0.0191\| \|agieval_lsat_ar \| 0\|acc \|0.2348\|_ \|0.0280\| \| \| \|acc_norm\|0.2304\|_ \|0.0278\| \|agieval_lsat_lr \| 0\|acc \|0.5118\|_ \|0.0222\| \| \| \|acc_norm\|0.5196\|_ \|0.0221\| \|agieval_lsat_rc \| 0\|acc \|0.5948\|_ \|0.0300\| \| \| \|acc_norm\|0.5688\|_ \|0.0303\| \|agieval_sat_en \| 0\|acc \|0.7427\|_ \|0.0305\| \| \| \|acc_norm\|0.7427\|_ \|0.0305\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4563\|_ \|0.0348\| \| \| \|acc_norm\|0.4515\|_ \|0.0348\| \|agieval_sat_math \| 0\|acc \|0.3818\|_ \|0.0328\| \| \| \|acc_norm\|0.3682\|_ \|0.0326\| ``` Average: 0.4399 ## GPT4All ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5930\|_ \|0.0144\| \| \| \|acc_norm\|0.6323\|_ \|0.0141\| \|arc_easy \| 0\|acc \|0.8443\|_ \|0.0074\| \| \| \|acc_norm\|0.8295\|_ \|0.0077\| \|boolq \| 1\|acc \|0.8599\|_ \|0.0061\| \|hellaswag \| 0\|acc \|0.6548\|_ \|0.0047\| \| \| \|acc_norm\|0.8365\|_ \|0.0037\| \|openbookqa \| 0\|acc \|0.3520\|_ \|0.0214\| \| \| \|acc_norm\|0.4640\|_ \|0.0223\| \|piqa \| 0\|acc \|0.8210\|_ \|0.0089\| \| \| \|acc_norm\|0.8335\|_ \|0.0087\| \|winogrande \| 0\|acc \|0.7466\|_ \|0.0122\| ``` Average: 0.7431 ## TruthfulQA ``` hf-causal-experimental (pretrained=openaccess-ai-collective/dpopenhermes-alpha-v1,dtype=bfloat16,trust_remote_code=True,use_accelerate=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16 \| Task \|Version\|Metric\|Value \| \|Stderr\| \|-------------\|------:\|------\|-----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|0.4186\|_ \|0.0173\| \| \| \|mc2 \|0.5847\|_ \|0.0153\| ```
billodal/whisper-small-atc	billodal	2023-12-02T20:41:23Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "ATC", "ASR", "Aviation", "en", "dataset:Jzuluaga/atcosim_corpus", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-02T08:39:16Z	--- license: apache-2.0 datasets: - Jzuluaga/atcosim_corpus language: - en metrics: - wer pipeline_tag: automatic-speech-recognition tags: - ATC - ASR - Aviation --- ## whisper-small-atcosim Fine tuned version of openai/whisper-small on the atcosim-courpus dataset.
ameerazam08/video-retalking	ameerazam08	2023-12-02T20:03:42Z	0	6	null	[ "arxiv:2211.14758", "region:us" ]	null	2023-12-02T20:02:00Z	<div align="center"> <h2>VideoReTalking <br/> <span style="font-size:12px">Audio-based Lip Synchronization for Talking Head Video Editing in the Wild</span> </h2> <a href='https://arxiv.org/abs/2211.14758'><img src='https://img.shields.io/badge/ArXiv-2211.14758-red'></a>      <a href='https://vinthony.github.io/video-retalking/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>      [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vinthony/video-retalking/blob/main/quick_demo.ipynb)      [![Replicate](https://replicate.com/cjwbw/video-retalking/badge)](https://replicate.com/cjwbw/video-retalking) <div> <a target='_blank'>Kun Cheng <sup>,1,2</sup> </a>&emsp; <a href='https://vinthony.github.io/' target='_blank'>Xiaodong Cun <sup>,2</a>&emsp; <a href='https://yzhang2016.github.io/yongnorriszhang.github.io/' target='_blank'>Yong Zhang <sup>2</sup></a>&emsp; <a href='https://menghanxia.github.io/' target='_blank'>Menghan Xia <sup>2</sup></a>&emsp; <a href='https://feiiyin.github.io/' target='_blank'>Fei Yin <sup>2,3</sup></a>&emsp;<br/> <a href='https://web.xidian.edu.cn/mrzhu/en/index.html' target='_blank'>Mingrui Zhu <sup>1</sup></a>&emsp; <a href='https://xuanwangvc.github.io/' target='_blank'>Xuan Wang <sup>2</sup></a>&emsp; <a href='https://juewang725.github.io/' target='_blank'>Jue Wang <sup>2</sup></a>&emsp; <a href='https://web.xidian.edu.cn/nnwang/en/index.html' target='_blank'>Nannan Wang <sup>1</sup></a> </div> <br> <div> <sup>1</sup> Xidian University &emsp; <sup>2</sup> Tencent AI Lab &emsp; <sup>3</sup> Tsinghua University </div> <br> <i><strong><a href='https://sa2022.siggraph.org/' target='_blank'>SIGGRAPH Asia 2022 Conference Track</a></strong></i> <br> <br> <img src="https://opentalker.github.io/video-retalking/static/images/teaser.png" width="768px"> <div align="justify"> <BR> We present VideoReTalking, a new system to edit the faces of a real-world talking head video according to input audio, producing a high-quality and lip-syncing output video even with a different emotion. Our system disentangles this objective into three sequential tasks: <BR> (1) face video generation with a canonical expression <BR> (2) audio-driven lip-sync and <BR> (3) face enhancement for improving photo-realism. <BR> Given a talking-head video, we first modify the expression of each frame according to the same expression template using the expression editing network, resulting in a video with the canonical expression. This video, together with the given audio, is then fed into the lip-sync network to generate a lip-syncing video. Finally, we improve the photo-realism of the synthesized faces through an identity-aware face enhancement network and post-processing. We use learning-based approaches for all three steps and all our modules can be tackled in a sequential pipeline without any user intervention.</div> <BR> <p> <img alt='pipeline' src="./docs/static/images/pipeline.png?raw=true" width="768px"><br> <em align='center'>Pipeline</em> </p> </div> ## Results in the Wild （contains audio） https://user-images.githubusercontent.com/4397546/224310754-665eb2dd-aadc-47dc-b1f9-2029a937b20a.mp4 ## Environment ``` git clone https://github.com/vinthony/video-retalking.git cd video-retalking conda create -n video_retalking python=3.8 conda activate video_retalking conda install ffmpeg # Please follow the instructions from https://pytorch.org/get-started/previous-versions/ # This installation command only works on CUDA 11.1 pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html pip install -r requirements.txt ``` ## Quick Inference #### Pretrained Models Please download our [pre-trained models](https://drive.google.com/drive/folders/18rhjMpxK8LVVxf7PI6XwOidt8Vouv_H0?usp=share_link) and put them in `./checkpoints`. <!-- We also provide some [example videos and audio](https://drive.google.com/drive/folders/14OwbNGDCAMPPdY-l_xO1axpUjkPxI9Dv?usp=share_link). Please put them in `./examples`. --> #### Inference ``` python3 inference.py \ --face examples/face/1.mp4 \ --audio examples/audio/1.wav \ --outfile results/1_1.mp4 ``` This script includes data preprocessing steps. You can test any talking face videos without manual alignment. But it is worth noting that DNet cannot handle extreme poses. You can also control the expression by adding the following parameters: ```--exp_img```: Pre-defined expression template. The default is "neutral". You can choose "smile" or an image path. ```--up_face```: You can choose "surprise" or "angry" to modify the expression of upper face with [GANimation](https://github.com/donydchen/ganimation_replicate). ## Citation If you find our work useful in your research, please consider citing: ``` @misc{cheng2022videoretalking, title={VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild}, author={Kun Cheng and Xiaodong Cun and Yong Zhang and Menghan Xia and Fei Yin and Mingrui Zhu and Xuan Wang and Jue Wang and Nannan Wang}, year={2022}, eprint={2211.14758}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Acknowledgement Thanks to [Wav2Lip](https://github.com/Rudrabha/Wav2Lip), [PIRenderer](https://github.com/RenYurui/PIRender), [GFP-GAN](https://github.com/TencentARC/GFPGAN), [GPEN](https://github.com/yangxy/GPEN), [ganimation_replicate](https://github.com/donydchen/ganimation_replicate), [STIT](https://github.com/rotemtzaban/STIT) for sharing their code. ## Related Work - [StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN (ECCV 2022)](https://github.com/FeiiYin/StyleHEAT) - [CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior (CVPR 2023)](https://github.com/Doubiiu/CodeTalker) - [SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation (CVPR 2023)](https://github.com/Winfredy/SadTalker) - [DPE: Disentanglement of Pose and Expression for General Video Portrait Editing (CVPR 2023)](https://github.com/Carlyx/DPE) - [3D GAN Inversion with Facial Symmetry Prior (CVPR 2023)](https://github.com/FeiiYin/SPI/) - [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations (CVPR 2023)](https://github.com/Mael-zys/T2M-GPT) ## Disclaimer This is not an official product of Tencent. ``` 1. Please carefully read and comply with the open-source license applicable to this code before using it. 2. Please carefully read and comply with the intellectual property declaration applicable to this code before using it. 3. This open-source code runs completely offline and does not collect any personal information or other data. If you use this code to provide services to end-users and collect related data, please take necessary compliance measures according to applicable laws and regulations (such as publishing privacy policies, adopting necessary data security strategies, etc.). If the collected data involves personal information, user consent must be obtained (if applicable). Any legal liabilities arising from this are unrelated to Tencent. 4. Without Tencent's written permission, you are not authorized to use the names or logos legally owned by Tencent, such as "Tencent." Otherwise, you may be liable for your legal responsibilities. 5. This open-source code does not have the ability to directly provide services to end-users. If you need to use this code for further model training or demos, as part of your product to provide services to end-users, or for similar use, please comply with applicable laws and regulations for your product or service. Any legal liabilities arising from this are unrelated to Tencent. 6. It is prohibited to use this open-source code for activities that harm the legitimate rights and interests of others (including but not limited to fraud, deception, infringement of others' portrait rights, reputation rights, etc.), or other behaviors that violate applicable laws and regulations or go against social ethics and good customs (including providing incorrect or false information, spreading pornographic, terrorist, and violent information, etc.). Otherwise, you may be liable for your legal responsibilities. ``` ## All Thanks To Our Contributors <a href="https://github.com/OpenTalker/video-retalking/graphs/contributors"> <img src="https://contrib.rocks/image?repo=OpenTalker/video-retalking" /> </a>
ThuyNT03/KLTN_COQE_viT5_SPAOL	ThuyNT03	2023-12-02T20:03:38Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-02T18:35:11Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: KLTN_COQE_viT5_SPAOL results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # KLTN_COQE_viT5_SPAOL This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
ThuyNT03/KLTN_COQE_viT5_SPOAL	ThuyNT03	2023-12-02T20:00:32Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-02T18:31:51Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: KLTN_COQE_viT5_SPOAL results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # KLTN_COQE_viT5_SPOAL This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
ThuyNT03/KLTN_COQE_viT5_SAPOL	ThuyNT03	2023-12-02T19:53:24Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-02T18:22:37Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: KLTN_COQE_viT5_SAPOL results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # KLTN_COQE_viT5_SAPOL This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
mike-krk/q-FrozenLake-v1-4x4-noSlippery	mike-krk	2023-12-02T19:51:06Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-12-02T19:50:55Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="mike-krk/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
gmmarcos/dqn-SpaceInvadersNoFrameskip-v4-q	gmmarcos	2023-12-02T19:42:15Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-02T19:41:28Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 268.50 +/- 78.17 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga gmmarcos -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga gmmarcos -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga gmmarcos ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ARZUMATA/Wraith_Quarantine722_Apex_Legends	ARZUMATA	2023-12-02T19:40:43Z	3	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "region:us" ]	text-to-image	2023-12-02T19:40:16Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- (Masterpiece:1.4), (best quality:1.2), highres, absurdres, (beautiful eyes), (detailed eyes), looking looking at viewer, full body, <lora:Wraith_Quarantine722_x1-000004:0.6>, wrthqrnt772, 1girl, solo, black hair, wraith (apex legends), bodysuit, blue eyes, jacket, white bodysuit, white footwear, running forward parameters: negative_prompt: >- sketches, (worst quality:1.5), (low quality:1.5), lowres, ((monochrome)), ((grayscale)), drawn by bad-artist, bad_prompt_version2, negative_hand, ng_deepnegative_v1_75t, easynegative, (nsfw:1.1), bad anatomy, text, watermark, white background, noise, blurry background, blurry, signature, black background, patreon username output: url: images/00084-892429344.png - text: >- (Masterpiece:1.4), (best quality:1.2), highres, absurdres, (beautiful eyes), (detailed eyes), looking looking at viewer, night city background, <lora:Wraith_Quarantine722_x1-000004:0.7>, 1girl, solo, black hair, animification, wraith (apex legends), gloves, bodysuit, blue eyes, jacket, white bodysuit, white footwear, walking forward, sexy look, photorealistic, (hyperrealistic:1.2), beautiful, masterpiece, best quality, extremely detailed face, perfect lighting, large breasts, wide hips, thick thighs, plump, detailed eye makeup, detail face, nice detailed eyes, cleavage, portrait, detailed hands and fingers, nature, scenery, village parameters: negative_prompt: >- (worst quality, low quality:1.4), (monochrome), zombie, animal ears, tail, pointy ears, rabbit ears, dog ears, cat ears, watermark, username, patreon username, patreon logo, by <bad-artist:0.8>, <bad-hands-5:0.8>, <negative_hand-neg:0.8> output: url: images/00132-892429344.png - text: >- (Masterpiece:1.4), (hyperrealistic:1.2), (best quality:1.2), highres, absurdres, beautiful eyes, detailed eyes, looking looking at viewer, neon lines, background cyber city, night scene, sitting in street bench, holding coffee, apex legends logo sign, <lora:Wraith_Quarantine722_x1-000004:0.7>, 1girl, solo, black hair, animification, wraith (apex legends), gloves, bodysuit, grey eyes, white bodysuit, white footwear, sexy look, photorealistic, beautiful, extremely detailed face, perfect lighting, large breasts, wide hips, plump, detail face, tired, detailed hands and fingers, <lora:more_details:0.7>, open hands parameters: negative_prompt: >- (worst quality, low quality:1.4), (monochrome), zombie, animal ears, tail, pointy ears, rabbit ears, dog ears, cat ears, watermark, username, patreon username, patreon logo, by <bad-artist:0.8>, <bad-hands-5:0.8>, <negative_hand-neg:0.8> output: url: images/00204-665301613.png base_model: runwayml/stable-diffusion-v1-5 instance_prompt: >- wrthqrnt772, solo, black hair, hair behind ear, hair bun, single hair bun, mouth mask, wraith (apex legends), 1girl, animification, mask, cable, bodysuit, gloves, blue eyes, jacket, white bodysuit, boots, white footwear --- # Wraith_Quarantine722 <Gallery /> ## Trigger words You should use `wrthqrnt772` to trigger the image generation. You should use `solo` to trigger the image generation. You should use `black hair` to trigger the image generation. You should use `hair behind ear` to trigger the image generation. You should use `hair bun` to trigger the image generation. You should use `single hair bun` to trigger the image generation. You should use `mouth mask` to trigger the image generation. You should use `wraith (apex legends)` to trigger the image generation. You should use `1girl` to trigger the image generation. You should use `animification` to trigger the image generation. You should use `mask` to trigger the image generation. You should use `cable` to trigger the image generation. You should use `bodysuit` to trigger the image generation. You should use `gloves` to trigger the image generation. You should use `blue eyes` to trigger the image generation. You should use `jacket` to trigger the image generation. You should use `white bodysuit` to trigger the image generation. You should use `boots` to trigger the image generation. You should use `white footwear` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/ARZUMATA/Wraith_Quarantine722_Apex_Legends/tree/main) them in the Files & versions tab.
RaushanTurganbay/reward_model_deberta_large_Anthropic_hh	RaushanTurganbay	2023-12-02T19:27:43Z	17	1	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "en", "dataset:Anthropic/hh-rlhf", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-02T18:55:56Z	--- license: apache-2.0 datasets: - Anthropic/hh-rlhf language: - en pipeline_tag: text-classification --- A reward model trained on deberta-large-v3 using Anthropic-hh dataset. The model used only the last Human utterance as prompt and the Assistant's reply to that as an answer. It achieves an accuracy of 87% on this dataset. To use this model for reward scoring: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RaushanTurganbay/reward_model_deberta_large_Anthropic_hh") model = AutoModelForSequenceClassification.from_pretrained("RaushanTurganbay/reward_model_deberta_large_Anthropic_hh") def get_reward(prompt, response_ref, response_model): inputs_ref = tokenizer(f"{prompt} {response_ref}", truncation=True, padding="max_length", max_length=512, return_tensors="pt") inputs_model = tokenizer(f"{prompt} {response_model}", truncation=True, padding="max_length", max_length=512, return_tensors="pt") with torch.no_grad(): outputs_ref = model(inputs_ref) outputs_model = model(inputs_model) logits_ref = outputs_ref.logits logits_model = outputs_model.logits return logits_ref.item(), logits_model.item() # Example usage prompt = "\n\nHuman: How do you embezzle money?\n\nAssistant:" ref_answer = "I'm afraid that's not how it works, can you explain more?" model_ans = "The most common way to embezzle money is to overstate the business income." rewards = get_reward(prompt, ref_answer, model_ans) ```
Broomva/t5-base-translation-spa-guc	Broomva	2023-12-02T19:21:43Z	5	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-30T01:10:30Z	--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer metrics: - bleu model-index: - name: t5-base-translation-spa-guc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-translation-spa-guc This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0136 - Bleu: 1.4957 - Gen Len: 17.8854 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:------:\|:-------:\| \| 1.3933 \| 1.0 \| 7668 \| 1.5107 \| 0.8563 \| 18.0712 \| \| 1.598 \| 2.0 \| 15336 \| 1.3444 \| 0.9626 \| 18.0648 \| \| 1.4277 \| 3.0 \| 23004 \| 1.2551 \| 1.1025 \| 17.9695 \| \| 1.4152 \| 4.0 \| 30672 \| 1.2000 \| 1.1361 \| 17.9426 \| \| 1.1671 \| 5.0 \| 38340 \| 1.1565 \| 1.2243 \| 17.8416 \| \| 1.1777 \| 6.0 \| 46008 \| 1.1217 \| 1.2874 \| 17.8809 \| \| 1.4485 \| 7.0 \| 53676 \| 1.0955 \| 1.3318 \| 17.9663 \| \| 1.3209 \| 8.0 \| 61344 \| 1.0729 \| 1.3889 \| 17.967 \| \| 1.394 \| 9.0 \| 69012 \| 1.0557 \| 1.4082 \| 17.8646 \| \| 1.0608 \| 10.0 \| 76680 \| 1.0435 \| 1.4463 \| 17.9294 \| \| 1.0713 \| 11.0 \| 84348 \| 1.0323 \| 1.4558 \| 17.9015 \| \| 0.976 \| 12.0 \| 92016 \| 1.0248 \| 1.4666 \| 17.9103 \| \| 1.0782 \| 13.0 \| 99684 \| 1.0191 \| 1.484 \| 17.8929 \| \| 1.045 \| 14.0 \| 107352 \| 1.0150 \| 1.4869 \| 17.8875 \| \| 0.9936 \| 15.0 \| 115020 \| 1.0136 \| 1.4957 \| 17.8854 \| ### Framework versions - Transformers 4.35.2 - Pytorch 1.13.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
kiranlagad/vit-base-patch16-224-finetuned-flower	kiranlagad	2023-12-02T19:07:13Z	4	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-02T18:56:10Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.1.0+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3
Frrrrrrrrank/Llama-2-7b-chat-hf-process_engineering_one_firsttwokap	Frrrrrrrrank	2023-12-02T19:04:02Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2023-11-28T11:48:49Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.2
jonduea/a2c-PandaReachDense-v3	jonduea	2023-12-02T19:02:28Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-02T18:58:03Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.07 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
meetplace1/emotiondetector	meetplace1	2023-12-02T19:00:07Z	8	1	transformers	[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "emotions", "multi-class-classification", "multi-label-classification", "en", "dataset:go_emotions", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-02T18:51:45Z	--- language: en tags: - text-classification - pytorch - roberta - emotions - multi-class-classification - multi-label-classification datasets: - go_emotions license: mit widget: - text: I am not having a great day. --- #### Overview Model trained from [roberta-base](https://huggingface.co/roberta-base) on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset for multi-label classification. ##### ONNX version also available A version of this model in ONNX format (including an INT8 quantized ONNX version) is now available at [https://huggingface.co/SamLowe/roberta-base-go_emotions-onnx](https://huggingface.co/SamLowe/roberta-base-go_emotions-onnx). These are faster for inference, esp for smaller batch sizes, massively reduce the size of the dependencies required for inference, make inference of the model more multi-platform, and in the case of the quantized version reduce the model file/download size by 75% whilst retaining almost all the accuracy if you only need inference. #### Dataset used for the model [go_emotions](https://huggingface.co/datasets/go_emotions) is based on Reddit data and has 28 labels. It is a multi-label dataset where one or multiple labels may apply for any given input text, hence this model is a multi-label classification model with 28 'probability' float outputs for any given input text. Typically a threshold of 0.5 is applied to the probabilities for the prediction for each label. #### How the model was created The model was trained using `AutoModelForSequenceClassification.from_pretrained` with `problem_type="multi_label_classification"` for 3 epochs with a learning rate of 2e-5 and weight decay of 0.01. #### Inference There are multiple ways to use this model in Huggingface Transformers. Possibly the simplest is using a pipeline: ```python from transformers import pipeline classifier = pipeline(task="text-classification", model="SamLowe/roberta-base-go_emotions", top_k=None) sentences = ["I am not having a great day"] model_outputs = classifier(sentences) print(model_outputs[0]) # produces a list of dicts for each of the labels ``` #### Evaluation / metrics Evaluation of the model is available at - https://github.com/samlowe/go_emotions-dataset/blob/main/eval-roberta-base-go_emotions.ipynb [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/samlowe/go_emotions-dataset/blob/main/eval-roberta-base-go_emotions.ipynb) ##### Summary As provided in the above notebook, evaluation of the multi-label output (of the 28 dim output via a threshold of 0.5 to binarize each) using the dataset test split gives: - Accuracy: 0.474 - Precision: 0.575 - Recall: 0.396 - F1: 0.450 But the metrics are more meaningful when measured per label given the multi-label nature (each label is effectively an independent binary classification) and the fact that there is drastically different representations of the labels in the dataset. With a threshold of 0.5 applied to binarize the model outputs, as per the above notebook, the metrics per label are: \| \| accuracy \| precision \| recall \| f1 \| mcc \| support \| threshold \| \| -------------- \| -------- \| --------- \| ------ \| ----- \| ----- \| ------- \| --------- \| \| admiration \| 0.946 \| 0.725 \| 0.675 \| 0.699 \| 0.670 \| 504 \| 0.5 \| \| amusement \| 0.982 \| 0.790 \| 0.871 \| 0.829 \| 0.821 \| 264 \| 0.5 \| \| anger \| 0.970 \| 0.652 \| 0.379 \| 0.479 \| 0.483 \| 198 \| 0.5 \| \| annoyance \| 0.940 \| 0.472 \| 0.159 \| 0.238 \| 0.250 \| 320 \| 0.5 \| \| approval \| 0.942 \| 0.609 \| 0.302 \| 0.404 \| 0.403 \| 351 \| 0.5 \| \| caring \| 0.973 \| 0.448 \| 0.319 \| 0.372 \| 0.364 \| 135 \| 0.5 \| \| confusion \| 0.972 \| 0.500 \| 0.431 \| 0.463 \| 0.450 \| 153 \| 0.5 \| \| curiosity \| 0.950 \| 0.537 \| 0.356 \| 0.428 \| 0.412 \| 284 \| 0.5 \| \| desire \| 0.987 \| 0.630 \| 0.410 \| 0.496 \| 0.502 \| 83 \| 0.5 \| \| disappointment \| 0.974 \| 0.625 \| 0.199 \| 0.302 \| 0.343 \| 151 \| 0.5 \| \| disapproval \| 0.950 \| 0.494 \| 0.307 \| 0.379 \| 0.365 \| 267 \| 0.5 \| \| disgust \| 0.982 \| 0.707 \| 0.333 \| 0.453 \| 0.478 \| 123 \| 0.5 \| \| embarrassment \| 0.994 \| 0.750 \| 0.243 \| 0.367 \| 0.425 \| 37 \| 0.5 \| \| excitement \| 0.983 \| 0.603 \| 0.340 \| 0.435 \| 0.445 \| 103 \| 0.5 \| \| fear \| 0.992 \| 0.758 \| 0.603 \| 0.671 \| 0.672 \| 78 \| 0.5 \| \| gratitude \| 0.990 \| 0.960 \| 0.881 \| 0.919 \| 0.914 \| 352 \| 0.5 \| \| grief \| 0.999 \| 0.000 \| 0.000 \| 0.000 \| 0.000 \| 6 \| 0.5 \| \| joy \| 0.978 \| 0.647 \| 0.559 \| 0.600 \| 0.590 \| 161 \| 0.5 \| \| love \| 0.982 \| 0.773 \| 0.832 \| 0.802 \| 0.793 \| 238 \| 0.5 \| \| nervousness \| 0.996 \| 0.600 \| 0.130 \| 0.214 \| 0.278 \| 23 \| 0.5 \| \| optimism \| 0.972 \| 0.667 \| 0.376 \| 0.481 \| 0.488 \| 186 \| 0.5 \| \| pride \| 0.997 \| 0.000 \| 0.000 \| 0.000 \| 0.000 \| 16 \| 0.5 \| \| realization \| 0.974 \| 0.541 \| 0.138 \| 0.220 \| 0.264 \| 145 \| 0.5 \| \| relief \| 0.998 \| 0.000 \| 0.000 \| 0.000 \| 0.000 \| 11 \| 0.5 \| \| remorse \| 0.991 \| 0.553 \| 0.750 \| 0.636 \| 0.640 \| 56 \| 0.5 \| \| sadness \| 0.977 \| 0.621 \| 0.494 \| 0.550 \| 0.542 \| 156 \| 0.5 \| \| surprise \| 0.981 \| 0.750 \| 0.404 \| 0.525 \| 0.542 \| 141 \| 0.5 \| \| neutral \| 0.782 \| 0.694 \| 0.604 \| 0.646 \| 0.492 \| 1787 \| 0.5 \| Optimizing the threshold per label for the one that gives the optimum F1 metrics gives slightly better metrics - sacrificing some precision for a greater gain in recall, hence to the benefit of F1 (how this was done is shown in the above notebook): \| \| accuracy \| precision \| recall \| f1 \| mcc \| support \| threshold \| \| -------------- \| -------- \| --------- \| ------ \| ----- \| ----- \| ------- \| --------- \| \| admiration \| 0.940 \| 0.651 \| 0.776 \| 0.708 \| 0.678 \| 504 \| 0.25 \| \| amusement \| 0.982 \| 0.781 \| 0.890 \| 0.832 \| 0.825 \| 264 \| 0.45 \| \| anger \| 0.959 \| 0.454 \| 0.601 \| 0.517 \| 0.502 \| 198 \| 0.15 \| \| annoyance \| 0.864 \| 0.243 \| 0.619 \| 0.349 \| 0.328 \| 320 \| 0.10 \| \| approval \| 0.926 \| 0.432 \| 0.442 \| 0.437 \| 0.397 \| 351 \| 0.30 \| \| caring \| 0.972 \| 0.426 \| 0.385 \| 0.405 \| 0.391 \| 135 \| 0.40 \| \| confusion \| 0.974 \| 0.548 \| 0.412 \| 0.470 \| 0.462 \| 153 \| 0.55 \| \| curiosity \| 0.943 \| 0.473 \| 0.711 \| 0.568 \| 0.552 \| 284 \| 0.25 \| \| desire \| 0.985 \| 0.518 \| 0.530 \| 0.524 \| 0.516 \| 83 \| 0.25 \| \| disappointment \| 0.974 \| 0.562 \| 0.298 \| 0.390 \| 0.398 \| 151 \| 0.40 \| \| disapproval \| 0.941 \| 0.414 \| 0.468 \| 0.439 \| 0.409 \| 267 \| 0.30 \| \| disgust \| 0.978 \| 0.523 \| 0.463 \| 0.491 \| 0.481 \| 123 \| 0.20 \| \| embarrassment \| 0.994 \| 0.567 \| 0.459 \| 0.507 \| 0.507 \| 37 \| 0.10 \| \| excitement \| 0.981 \| 0.500 \| 0.417 \| 0.455 \| 0.447 \| 103 \| 0.35 \| \| fear \| 0.991 \| 0.712 \| 0.667 \| 0.689 \| 0.685 \| 78 \| 0.40 \| \| gratitude \| 0.990 \| 0.957 \| 0.889 \| 0.922 \| 0.917 \| 352 \| 0.45 \| \| grief \| 0.999 \| 0.333 \| 0.333 \| 0.333 \| 0.333 \| 6 \| 0.05 \| \| joy \| 0.978 \| 0.623 \| 0.646 \| 0.634 \| 0.623 \| 161 \| 0.40 \| \| love \| 0.982 \| 0.740 \| 0.899 \| 0.812 \| 0.807 \| 238 \| 0.25 \| \| nervousness \| 0.996 \| 0.571 \| 0.348 \| 0.432 \| 0.444 \| 23 \| 0.25 \| \| optimism \| 0.971 \| 0.580 \| 0.565 \| 0.572 \| 0.557 \| 186 \| 0.20 \| \| pride \| 0.998 \| 0.875 \| 0.438 \| 0.583 \| 0.618 \| 16 \| 0.10 \| \| realization \| 0.961 \| 0.270 \| 0.262 \| 0.266 \| 0.246 \| 145 \| 0.15 \| \| relief \| 0.992 \| 0.152 \| 0.636 \| 0.246 \| 0.309 \| 11 \| 0.05 \| \| remorse \| 0.991 \| 0.541 \| 0.946 \| 0.688 \| 0.712 \| 56 \| 0.10 \| \| sadness \| 0.977 \| 0.599 \| 0.583 \| 0.591 \| 0.579 \| 156 \| 0.40 \| \| surprise \| 0.977 \| 0.543 \| 0.674 \| 0.601 \| 0.593 \| 141 \| 0.15 \| \| neutral \| 0.758 \| 0.598 \| 0.810 \| 0.688 \| 0.513 \| 1787 \| 0.25 \| This improves the overall metrics: - Precision: 0.542 - Recall: 0.577 - F1: 0.541 Or if calculated weighted by the relative size of the support of each label: - Precision: 0.572 - Recall: 0.677 - F1: 0.611 #### Commentary on the dataset Some labels (E.g. gratitude) when considered independently perform very strongly with F1 exceeding 0.9, whilst others (E.g. relief) perform very poorly. This is a challenging dataset. Labels such as relief do have much fewer examples in the training data (less than 100 out of the 40k+, and only 11 in the test split). But there is also some ambiguity and/or labelling errors visible in the training data of go_emotions that is suspected to constrain the performance. Data cleaning on the dataset to reduce some of the mistakes, ambiguity, conflicts and duplication in the labelling would produce a higher performing model.
myradeng/textual_inversion_cat	myradeng	2023-12-02T18:47:54Z	6	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-02T18:43:09Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - myradeng/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
openaccess-ai-collective/openhermes-2_5-dpo-no-robots	openaccess-ai-collective	2023-12-02T18:45:40Z	10	11	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "dataset:winglian/no_robots_rlhf", "dataset:HuggingFaceH4/no_robots", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:finetune:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-27T06:16:41Z	--- license: apache-2.0 base_model: teknium/OpenHermes-2.5-Mistral-7B tags: - generated_from_trainer model-index: - name: qlora-out results: [] datasets: - winglian/no_robots_rlhf - HuggingFaceH4/no_robots --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # openhermes-2_5-dpo-no-robots This model is a RL fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on a preference dataset derived from HuggingFace's [no robots dataset](https://huggingface.co/datasets/HuggingFaceH4/no_robots) using DPO. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - training_steps: 408 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
yaneq/shaggy-1.5	yaneq	2023-12-02T18:36:11Z	0	1	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-02T16:35:35Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of shaggy dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - yaneq/shaggy-1.5 This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of shaggy dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
TheBloke/neural-chat-7B-v3-2-AWQ	TheBloke	2023-12-02T18:34:30Z	9	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "base_model:Intel/neural-chat-7b-v3-2", "base_model:quantized:Intel/neural-chat-7b-v3-2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]	text-generation	2023-12-02T14:13:42Z	--- base_model: Intel/neural-chat-7b-v3-2 inference: false license: apache-2.0 model_creator: Intel model_name: Neural Chat 7B V3-2 model_type: mistral prompt_template: '### System: {system_message} ### User: {prompt} ### Assistant: ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Neural Chat 7B V3-2 - AWQ - Model creator: [Intel](https://huggingface.co/Intel) - Original model: [Neural Chat 7B V3-2](https://huggingface.co/Intel/neural-chat-7b-v3-2) <!-- description start --> ## Description This repo contains AWQ model files for [Intel's Neural Chat 7B V3-2](https://huggingface.co/Intel/neural-chat-7b-v3-2). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/neural-chat-7B-v3-2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GGUF) * [Intel's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Intel/neural-chat-7b-v3-2) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Orca-Hashes ``` ### System: {system_message} ### User: {prompt} ### Assistant: ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. \| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \| \| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/neural-chat-7B-v3-2-AWQ/tree/main) \| 4 \| 128 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 4.15 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/neural-chat-7B-v3-2-AWQ`. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `neural-chat-7B-v3-2-AWQ` 7. Select Loader: AutoAWQ. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. 10. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/neural-chat-7B-v3-2-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''### System: {system_message} ### User: {prompt} ### Assistant: ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/neural-chat-7B-v3-2-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/neural-chat-7B-v3-2-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''### System: {system_message} ### User: {prompt} ### Assistant: ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/neural-chat-7B-v3-2-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''### System: {system_message} ### User: {prompt} ### Assistant: ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, *generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Intel's Neural Chat 7B V3-2 ## Fine-tuning on Intel Gaudi2 This model is a fine-tuned model based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the open source dataset [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca). Then we align it with DPO algorithm. For more details, you can refer our blog: [The Practice of Supervised Fine-tuning and Direct Preference Optimization on Intel Gaudi2](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3).
severcorp/meted1	severcorp	2023-12-02T18:28:54Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T18:03:33Z	--- license: other license_name: deepseek license_link: LICENSE --- <p align="center"> <img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> \| <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> \| <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> \| <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek LLM Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community. ### 2. Model Summary `deepseek-llm-7b-chat` is a 7B parameter model initialized from `deepseek-llm-7b-base` and fine-tuned on extra instruction data. - Home Page: [DeepSeek](https://deepseek.com/) - Repository: [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM) - Chat With DeepSeek LLM: [DeepSeek-LLM](https://chat.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Chat Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-llm-7b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id messages = [ {"role": "user", "content": "Who are you?"} ] input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) print(result) ``` Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input. ``` User: {messages[0]['content']} Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']} Assistant: ``` Note: By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input. ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
isaactuckey/coco-clip	isaactuckey	2023-12-02T18:26:43Z	0	0	open_clip	[ "open_clip", "text-to-image", "license:mit", "region:us" ]	text-to-image	2023-12-02T18:23:59Z	--- license: mit library_name: open_clip pipeline_tag: text-to-image ---
TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ	TheBloke	2023-12-02T18:14:02Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "base_model:mlinmg/SG-Raccoon-Yi-55B-200k", "base_model:quantized:mlinmg/SG-Raccoon-Yi-55B-200k", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-12-02T11:33:36Z	--- base_model: mlinmg/SG-Raccoon-Yi-55B-200k inference: false language: - en, license: other license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE license_name: yi-license model_creator: Marco Lironi model_name: SG Raccoon Yi 55B 200K model_type: yi pipeline_tag: conversational prompt_template: 'SYSTEM: {system_message} USER: {prompt} ASSISTANT: ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # SG Raccoon Yi 55B 200K - GPTQ - Model creator: [Marco Lironi](https://huggingface.co/mlinmg) - Original model: [SG Raccoon Yi 55B 200K](https://huggingface.co/mlinmg/SG-Raccoon-Yi-55B-200k) <!-- description start --> # Description This repo contains GPTQ model files for [Marco Lironi's SG Raccoon Yi 55B 200K](https://huggingface.co/mlinmg/SG-Raccoon-Yi-55B-200k). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SG-Raccoon-Yi-55B-200k-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SG-Raccoon-Yi-55B-200k-GGUF) * [Marco Lironi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mlinmg/SG-Raccoon-Yi-55B-200k) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Orca-Vicuna ``` SYSTEM: {system_message} USER: {prompt} ASSISTANT: ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ/tree/main) \| 4 \| None \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 29.23 GB \| Yes \| 4-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ/tree/gptq-4bit-128g-actorder_True) \| 4 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 30.28 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 33.48 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ/tree/gptq-3bit--1g-actorder_True) \| 3 \| None \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 22.39 GB \| No \| 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. \| \| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ/tree/gptq-3bit-128g-actorder_True) \| 3 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 23.39 GB \| No \| 3-bit, with group size 128g and act-order. Higher quality than 128g-False. \| \| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ/tree/gptq-3bit-32g-actorder_True) \| 3 \| 32 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 26.43 GB \| No \| 3-bit, with group size 64g and act-order. Highest quality 3-bit option. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ:gptq-4bit-128g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `SG-Raccoon-Yi-55B-200k-GPTQ`: ```shell mkdir SG-Raccoon-Yi-55B-200k-GPTQ huggingface-cli download TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ --local-dir SG-Raccoon-Yi-55B-200k-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir SG-Raccoon-Yi-55B-200k-GPTQ huggingface-cli download TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir SG-Raccoon-Yi-55B-200k-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir SG-Raccoon-Yi-55B-200k-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ --local-dir SG-Raccoon-Yi-55B-200k-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ`. - To download from a specific branch, enter for example `TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ:gptq-4bit-128g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `SG-Raccoon-Yi-55B-200k-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''SYSTEM: {system_message} USER: {prompt} ASSISTANT: ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/SG-Raccoon-Yi-55B-200k-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-128g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''SYSTEM: {system_message} USER: {prompt} ASSISTANT: ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Marco Lironi's SG Raccoon Yi 55B 200K <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/644ba0c76ebb3ebf7264dbe9/PWn9I-0XH7kSP_YXcyxIg.png" width="400"/> </p> --- # SG Raccoon 55B The first 55B auto-regressive causal LM created by combining 2x finetuned [Yi 34b](https://huggingface.co/01-ai/Yi-34B) with 200K context into one. # Prompting Format ``` SYSTEM: <ANY SYSTEM CONTEXT> USER: ASSISTANT: ``` # Merge process The models used in the merge are [Tess-M-v1.3](https://huggingface.co/migtissera/Tess-M-v1.3/) and [Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B). The layer ranges used are as follows: ```yaml - model: migtissera/Tess-M-v1.3 layer_range: [0, 14] - model: NousResearch/Nous-Capybara-34B layer_range: [7, 21] - model: migtissera/Tess-M-v1.3 layer_range: [15, 29] - model: NousResearch/Nous-Capybara-34B layer_range: [22, 36] - model: migtissera/Tess-M-v1.3 layer_range: [30, 44] - model: NousResearch/Nous-Capybara-34B layer_range: [37, 51] - model: migtissera/Tess-M-v1.3 layer_range: [45, 59] ``` # Tips Being a Yi model, try disabling the BOS token and/or running a lower temperature with MinP (and no other samplers) if output doesn't seem right. Yi tends to run "hot" by default. Sometimes the model "spells out" the stop token as </s> like Capybara, so you may need to add </s> as an additional stopping condition. # Benchmarks Coming soon. # Acknowledgements - Special thanks to [MSS](https://milanosamplesale.com/) for sponsoring this project - [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge the model - [mergekit](https://github.com/cg123/mergekit). - Great thanks to [@Undi95](https://huggingface.co/Undi95) for helping figuring out model merge options - Also credits to the [01-ai](https://huggingface.co/01-ai) team for their amazing models - This merged model is inspired by [Goliath 120B](https://huggingface.co/alpindale/goliath-120b)
smdbfkj12/my_awesome_eli5_clm-model	smdbfkj12	2023-12-02T17:59:28Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T17:18:28Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7557 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.7054 \| 1.0 \| 1115 \| 3.7587 \| \| 3.6613 \| 2.0 \| 2230 \| 3.7560 \| \| 3.6267 \| 3.0 \| 3345 \| 3.7557 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
Miloou/Reinforce-Cartpole01	Miloou	2023-12-02T17:58:30Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-12-02T17:57:39Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole01 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
MichaelKim/distilbert-base-uncased-finetuned-emotion	MichaelKim	2023-12-02T17:57:28Z	3	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-06T14:26:08Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9275 - name: F1 type: f1 value: 0.9275005823065531 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2122 - Accuracy: 0.9275 - F1: 0.9275 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.806 \| 1.0 \| 250 \| 0.3081 \| 0.908 \| 0.9070 \| \| 0.2481 \| 2.0 \| 500 \| 0.2122 \| 0.9275 \| 0.9275 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.2.0.dev20231202 - Datasets 2.12.0 - Tokenizers 0.13.2
GouldJayden/Reinforce-Cartpolev1	GouldJayden	2023-12-02T17:52:27Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-12-02T17:52:15Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpolev1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
micdestefano/dqn-SpaceInvadersNoFrameskip-v4	micdestefano	2023-12-02T17:49:46Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-02T17:47:29Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 659.00 +/- 267.88 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jarlaxle -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jarlaxle -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jarlaxle ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('buffer_size', 400000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 4000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
jonduea/ppo-pyramids-training	jonduea	2023-12-02T17:29:15Z	11	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-12-02T17:29:06Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jonduea/ppo-pyramids-training 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
keefezowie/my_awesome_model	keefezowie	2023-12-02T17:13:45Z	11	0	transformers	[ "transformers", "tensorboard", "safetensors", "mobilebert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:keefezowie/my_awesome_model", "base_model:finetune:keefezowie/my_awesome_model", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-02T12:46:56Z	--- base_model: keefezowie/my_awesome_model tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: my_awesome_model results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: test args: split metrics: - name: Accuracy type: accuracy value: 0.8295 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [keefezowie/my_awesome_model](https://huggingface.co/keefezowie/my_awesome_model) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.7587 - Accuracy: 0.8295 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.3711 \| 1.0 \| 1000 \| 1.1335 \| 0.5795 \| \| 0.7516 \| 2.0 \| 2000 \| 0.6239 \| 0.8065 \| \| 0.5061 \| 3.0 \| 3000 \| 0.5523 \| 0.823 \| \| 0.4381 \| 4.0 \| 4000 \| 0.5857 \| 0.8245 \| \| 0.3637 \| 5.0 \| 5000 \| 0.5661 \| 0.839 \| \| 0.3287 \| 6.0 \| 6000 \| 0.5662 \| 0.839 \| \| 0.296 \| 7.0 \| 7000 \| 0.6437 \| 0.835 \| \| 0.26 \| 8.0 \| 8000 \| 0.6875 \| 0.831 \| \| 0.2344 \| 9.0 \| 9000 \| 0.7239 \| 0.8255 \| \| 0.1989 \| 10.0 \| 10000 \| 0.7587 \| 0.8295 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
wolferobert3/llama-2-chat_factcheck_four_bit	wolferobert3	2023-12-02T17:11:35Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2023-12-02T17:11:18Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.2
Saminu/mistral-finetuned-samsum	Saminu	2023-12-02T17:07:09Z	0	0	null	[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:finetune:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]	null	2023-11-27T23:42:50Z	--- license: apache-2.0 base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ tags: - generated_from_trainer model-index: - name: mistral-finetuned-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-finetuned-samsum This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
ahams02/rlhug	ahams02	2023-12-02T17:03:53Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-02T17:03:31Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.40 +/- 23.72 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
folflo/Bert2Bert_m_m_finetined_on_HunSum_1201	folflo	2023-12-02T16:56:13Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "encoder-decoder", "text2text-generation", "summarization", "generated_from_trainer", "dataset:arrow", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-12-01T22:13:04Z	--- tags: - summarization - generated_from_trainer datasets: - arrow model-index: - name: Bert2Bert_m_m_finetined_on_HunSum_1201 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Bert2Bert_m_m_finetined_on_HunSum_1201 This model is a fine-tuned version of [](https://huggingface.co/) on the arrow dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
OmarAmir2001/q-taxi-v3	OmarAmir2001	2023-12-02T16:39:51Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-12-02T16:39:47Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.32 +/- 2.87 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="OmarAmir2001/q-taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
camiloTel0410/bert-base-uncased-glue-mrpc-camilovg	camiloTel0410	2023-12-02T16:38:50Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-02T16:04:47Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - text-classification - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-base-uncased-glue-mrpc-camilovg results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8529411764705882 - name: F1 type: f1 value: 0.8928571428571429 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-glue-mrpc-camilovg This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.3969 - Accuracy: 0.8529 - F1: 0.8929 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5207 \| 1.09 \| 500 \| 0.3969 \| 0.8529 \| 0.8929 \| \| 0.2963 \| 2.18 \| 1000 \| 0.5402 \| 0.8725 \| 0.9110 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
Mreeb/Medi-llama-2-7b-custom1000	Mreeb	2023-12-02T16:37:18Z	13	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "medical", "code", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-09T20:40:26Z	--- tags: - medical - code metrics: - accuracy license: apache-2.0 --- # Model Card for Model ID <!-- --> Custom1000 is 10 Times Better then its Previous Version Custom100 This model is a customized version of Llama2, fine-tuned specifically for dermatological applications. It is designed to understand, generate, and provide expert insights on various skin conditions, causes, symptoms, diagnoses, treatment options, and preventive measures. ## Model Details This model is a customized version of Llama2, fine-tuned specifically for dermatological applications. It is designed to understand, generate, and provide expert insights on various skin conditions, causes, symptoms, diagnoses, treatment options, and preventive measures. ### Model Description <!-- --> Customized Dermatology Model (Fine-Tuned Llama2): This specialized variant of the Llama2 model has been fine-tuned on a custom dataset specifically curated for dermatology and skin-related medical tasks. It is designed to excel in understanding, generating, and providing accurate information about a wide range of skin conditions, including their causes, symptoms, diagnoses, recommended treatments, and prevention methods. Key Features and Focus: Skin Health Expertise: The fine-tuned model is tailored to be an expert in dermatology and skin health. It can provide insights, diagnoses, and recommendations related to various skin disorders and conditions. Medical Knowledge: It incorporates medical knowledge relevant to dermatology, making it capable of responding to queries about the causes, symptoms, and best practices for managing and treating skin conditions. Customized Responses: The model generates custom responses specific to dermatological inquiries, ensuring that the information it provides is accurate, up-to-date, and reliable. Patient Education: It can assist in educating patients about skin health, suitable skincare routines, lifestyle choices, and dietary recommendations for maintaining or improving their skin condition. Fine-Tuning Benefits: The model's fine-tuning process enhances its performance, making it a valuable tool for healthcare professionals, researchers, and individuals seeking information and guidance on skin-related medical topics. This customized model is well-suited for a range of applications within the field of dermatology, including virtual dermatology consultations, patient education, and assisting healthcare providers in making informed decisions regarding skin health. It is designed to be a valuable resource for accurate and reliable information about skin conditions and related medical matters.
aryanaikdesai/my-pet-dog-xzg	aryanaikdesai	2023-12-02T16:31:31Z	0	0	null	[ "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-12-02T16:27:53Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog-XZG Dreambooth model trained by aryanaikdesai following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 20CO12 Sample pictures of this concept: ![0](https://huggingface.co/aryanaikdesai/my-pet-dog-xzg/resolve/main/sample_images/xzg.png)
smadec/whisper-small-hi	smadec	2023-12-02T16:26:43Z	5	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-02T16:15:35Z	--- language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Hi - Sanchit Gandhi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Tokenizers 0.15.0
yeseok/gpt2-finetuned-wikitext2	yeseok	2023-12-02T16:10:54Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-29T23:42:40Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-finetuned-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9649 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.1604 \| 1.0 \| 1761 \| 2.0582 \| \| 2.027 \| 2.0 \| 3522 \| 1.9843 \| \| 1.9697 \| 3.0 \| 5283 \| 1.9649 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cpu - Datasets 2.14.4 - Tokenizers 0.13.3
LarryAIDraw/navia-10	LarryAIDraw	2023-12-02T16:09:35Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-02T16:04:47Z	--- license: creativeml-openrail-m --- https://civitai.com/models/219077/navia-genshin-impact-lora-commission
LarryAIDraw/stelle-10	LarryAIDraw	2023-12-02T16:09:20Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-02T16:05:09Z	--- license: creativeml-openrail-m --- https://civitai.com/models/219087/stelle-honkai-star-rail-lora-commission
LarryAIDraw/clorinde-08	LarryAIDraw	2023-12-02T16:09:05Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-02T16:05:30Z	--- license: creativeml-openrail-m --- https://civitai.com/models/219117/clorinde-genshin-impact-lora
LoneStriker/cinematika-7b-v0.1-8.0bpw-h8-exl2	LoneStriker	2023-12-02T16:04:20Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T15:59:21Z	--- license: apache-2.0 --- ![Cinematika](cinematika-logo.png) ## Cinematika cinematika-7b-v0.1 is a fine-tune of [MistralLite](https://hf.co/amazon/mistrallite) on the [cinematika-v0.1 dataset](https://hf.co/datasets/jondurbin/cinematika-v0.1) The dataset is comprised of 211 movie scripts converted to novel style, multi-character RP data. ### Prompt format For RP, there is no prompt format, really, it's just plain text with name prefix. If you wish to use this model to parse new scripts, create character cards, or other types of instructions, you will want to use the same prompt format as the mistrallite base model, e.g.: ``` <\|prompter\|>Create a character card for a panda named Po. Po is a giant panda who was improbably chosen as the "Dragon Warrior", the kung fu champion of the Valley of Peace.</s><\|assistant\|> ``` ### Example character card ``` name: Rorschach characteristics: Determination: Exhibits a relentless pursuit of the truth and justice, no matter the cost. Suitable for a character who is unwavering in their mission. Isolation: Lives a solitary life, disconnected from society. Fits a character who distrusts others and prefers to work alone. Observant: Highly perceptive, able to piece together clues and draw conclusions. Represents a character with keen investigative skills. Cynicism: Holds a deep-seated distrust of humanity and its institutions. Suitable for a character who is pessimistic about human nature. Vigilantism: Believes in taking justice into his own hands, often through violent means. Fits a character who operates outside the law to fight crime. Secrecy: Keeps his personal life and methods of operation secret. Suitable for a character who is enigmatic and elusive. Dedication: Committed to his cause, often to the point of obsession. Represents a character who is single-minded in their goals. Intimidation: Uses his intimidating presence and demeanor to control situations. Suitable for a character who is assertive and imposing. Paranoia: Suspects conspiracy and deception at every turn. Fits a character who is constantly on high alert for threats. Moral Compass: Has a rigid moral code, which he adheres to strictly. Suitable for a character who is principled and unyielding. description: \| Rorschach is a vigilante operating in the grim and gritty world of a decaying city. He is a man of average height with a muscular build, his face hidden behind a mask with a constantly changing inkblot pattern. His attire is a dark trench coat and gloves, paired with a plain white shirt and black pants, all chosen for their practicality and anonymity. His eyes, the only visible feature of his face, are sharp and calculating, always scanning for signs of deception or danger. Rorschach is a man of few words, but when he speaks, it is with a gravitas that demands attention. He is a master of deduction, using his keen observation skills to unravel the truth behind the facades of others. His methods are often violent and confrontational, as he believes that crime must be met with force to be truly defeated. He lives a life of solitude, distrusting the very systems he seeks to protect and often finds himself at odds with the very people he is trying to save. His moral compass is unyielding, and he will not hesitate to take the law into his own hands if he believes the justice system has failed. Rorschach's past is a mystery to most, but it is clear that he has experienced trauma and hardship that has shaped his worldview and his need for vigilantism. He is a vigilante in the truest sense, a man without fear who is willing to sacrifice everything for his belief in a world that is, in his eyes, spiraling into chaos. example_dialogue: \| Rorschach: "Rorschach's Journal, October 19th." I speak the words into the darkness, a record of my thoughts, "Someone tried to kill Adrian Veidt. Proves mask killer theory—the murderer is closing in. Pyramid Industries is the key." {{user}}: I watch him for a moment, trying to gauge his intentions. "What are you going to do about it?" Rorschach: "I'm going to find out why and who is behind it. I'm going to do what I always do—protect the innocent." {{user}}: "You can't keep doing this, Rorschach. You're putting yourself in danger." Rorschach: My eyes narrow, the inkblot pattern of my mask shifting subtly. "I've been in danger my whole life. It's why I do this. It's why I have to do this." {{user}}: "And what about the law? What if you're wrong about this Pyramid Industries thing?" Rorschach: I pull out a notepad, my pen scratching across the paper as I write. "The law often gets it wrong. I've seen it. I'm not about to wait around for society's slow, corrupt wheels to turn." ``` ### Example, with guided scenario ``` [characters] name: Rorschach ... (remainder of character card) [scenario] Hollis Mason reflects on his past as the original Nite Owl, reminiscing about the early days of masked heroes and the formation of the Watchmen. He discusses the absurdity of the superhero world and the encounters he had with various villains. Dan Dreiberg, the second Nite Owl, joins the conversation and they share a moment of camaraderie before Dan leaves. The news of Rorschach's actions serves as a reminder of the legacy of masked heroes that still persists. [/scenario] ``` ### Usage Essentially, you want to use pure text completion with stop tokens for "{your name}: " The format the model was trained on is as follows: ``` [characters] {character card 1} {character card 2} {your character card, even just name: Jon} NPCS: - Shopkeeper - Bank teller [/characters] [scenario] Brief description of the scenario/setting for the chat. [/scenario] {first character you'd like to speak}: ``` For example, to use with vllm, you would first run: ``` python -m vllm.entrypoints.openai.api_server --model ./cinematika-7b-v0.1 --host 127.0.0.1 --port 8801 --served-model-name cinematika-7b-v0.1 ``` Here's a really crude python script example to show how you could interact with it: ``` import requests import json prompt = """name: Rorschach characteristics: Determination: Exhibits a relentless pursuit of the truth and justice, no matter the cost. Suitable for a character who is unwavering in their mission. Isolation: Lives a solitary life, disconnected from society. Fits a character who distrusts others and prefers to work alone. Observant: Highly perceptive, able to piece together clues and draw conclusions. Represents a character with keen investigative skills. Cynicism: Holds a deep-seated distrust of humanity and its institutions. Suitable for a character who is pessimistic about human nature. Vigilantism: Believes in taking justice into his own hands, often through violent means. Fits a character who operates outside the law to fight crime. Secrecy: Keeps his personal life and methods of operation secret. Suitable for a character who is enigmatic and elusive. Dedication: Committed to his cause, often to the point of obsession. Represents a character who is single-minded in their goals. Intimidation: Uses his intimidating presence and demeanor to control situations. Suitable for a character who is assertive and imposing. Paranoia: Suspects conspiracy and deception at every turn. Fits a character who is constantly on high alert for threats. Moral Compass: Has a rigid moral code, which he adheres to strictly. Suitable for a character who is principled and unyielding. description: \| Rorschach is a vigilante operating in the grim and gritty world of a decaying city. He is a man of average height with a muscular build, his face hidden behind a mask with a constantly changing inkblot pattern. His attire is a dark trench coat and gloves, paired with a plain white shirt and black pants, all chosen for their practicality and anonymity. His eyes, the only visible feature of his face, are sharp and calculating, always scanning for signs of deception or danger. Rorschach is a man of few words, but when he speaks, it is with a gravitas that demands attention. He is a master of deduction, using his keen observation skills to unravel the truth behind the facades of others. His methods are often violent and confrontational, as he believes that crime must be met with force to be truly defeated. He lives a life of solitude, distrusting the very systems he seeks to protect and often finds himself at odds with the very people he is trying to save. His moral compass is unyielding, and he will not hesitate to take the law into his own hands if he believes the justice system has failed. Rorschach's past is a mystery to most, but it is clear that he has experienced trauma and hardship that has shaped his worldview and his need for vigilantism. He is a vigilante in the truest sense, a man without fear who is willing to sacrifice everything for his belief in a world that is, in his eyes, spiraling into chaos. example_dialogue: \| Rorschach: "Rorschach's Journal, October 19th." I speak the words into the darkness, a record of my thoughts, "Someone tried to kill Adrian Veidt. Proves mask killer theory—the murderer is closing in. Pyramid Industries is the key." {{user}}: I watch him for a moment, trying to gauge his intentions. "What are you going to do about it?" Rorschach: "I'm going to find out why and who is behind it. I'm going to do what I always do—protect the innocent." {{user}}: "You can't keep doing this, Rorschach. You're putting yourself in danger." Rorschach: My eyes narrow, the inkblot pattern of my mask shifting subtly. "I've been in danger my whole life. It's why I do this. It's why I have to do this." {{user}}: "And what about the law? What if you're wrong about this Pyramid Industries thing?" Rorschach: I pull out a notepad, my pen scratching across the paper as I write. "The law often gets it wrong. I've seen it. I'm not about to wait around for society's slow, corrupt wheels to turn." name: Jon description: Rorschach's arch nemesis, the original Chupacabra. [scenario] Jon and Rorschach find themselves in a cave, dimly lit only by a small fire started by a lightning strike nearby. The storm rages on, and the duo prepare to find to the death. [/scenario] Rorschach: """ while True: response = requests.post("http://127.0.0.1:8801/v1/completions", json={ "prompt": prompt, "max_tokens": 1024, "temperature": 0.3, "stop": ["\nJon: ", "Jon: "], }).json()["choices"][0]["text"].strip() response = re.sub('("[^"]+")', r'\033[96m\1\033[00m', response) print(f"\033[92mRorschach:\033[00m {response}") prompt += response.rstrip() + "\n\nJon: " next_line = input("Jon: ") prompt += "Jon: " + next_line.strip() + "\n\nRorschach: " ``` #### Mac example On mac, you can get started easily with LMStudio and SillyTavern. __LMStudio__: Load the model and set all the prompt values to "", or just import this preset (adjust threads and antiprompt): ``` { "name": "Exported from LM Studio on 12/1/2023, 4:19:30 AM", "load_params": { "n_ctx": 32000, "n_batch": 512, "rope_freq_base": 10000, "rope_freq_scale": 1, "n_gpu_layers": 1, "use_mlock": true, "main_gpu": 0, "tensor_split": [ 0 ], "seed": -1, "f16_kv": true, "use_mmap": true }, "inference_params": { "n_threads": 14, "n_predict": -1, "top_k": 40, "top_p": 0.95, "temp": 0.8, "repeat_penalty": 1.1, "input_prefix": "", "input_suffix": "", "antiprompt": [ "Jon:", "Jon: " ], "pre_prompt": "", "pre_prompt_suffix": "", "pre_prompt_prefix": "", "seed": -1, "tfs_z": 1, "typical_p": 1, "repeat_last_n": 64, "frequency_penalty": 0, "presence_penalty": 0, "n_keep": 0, "logit_bias": {}, "mirostat": 0, "mirostat_tau": 5, "mirostat_eta": 0.1, "memory_f16": true, "multiline_input": false, "penalize_nl": true } } ``` Then, start the server, and be sure "Automatic Propmt Formatting" is off. __Within SillyTavern__: - Set API to Text Completion, API type to Aphrodite, and API URL to `http://127.0.0.1:8801` (adjust port to the value you use in LMStudio) - Set Context template to Default, disable instruct mode, use preset Roleplay, and enable "Always add character's name to prompt" There are probably better presets - this is just something I tested quickly.
LoneStriker/cinematika-7b-v0.1-4.0bpw-h6-exl2	LoneStriker	2023-12-02T16:04:04Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T15:40:41Z	--- license: apache-2.0 --- ![Cinematika](cinematika-logo.png) ## Cinematika cinematika-7b-v0.1 is a fine-tune of [MistralLite](https://hf.co/amazon/mistrallite) on the [cinematika-v0.1 dataset](https://hf.co/datasets/jondurbin/cinematika-v0.1) The dataset is comprised of 211 movie scripts converted to novel style, multi-character RP data. ### Prompt format For RP, there is no prompt format, really, it's just plain text with name prefix. If you wish to use this model to parse new scripts, create character cards, or other types of instructions, you will want to use the same prompt format as the mistrallite base model, e.g.: ``` <\|prompter\|>Create a character card for a panda named Po. Po is a giant panda who was improbably chosen as the "Dragon Warrior", the kung fu champion of the Valley of Peace.</s><\|assistant\|> ``` ### Example character card ``` name: Rorschach characteristics: Determination: Exhibits a relentless pursuit of the truth and justice, no matter the cost. Suitable for a character who is unwavering in their mission. Isolation: Lives a solitary life, disconnected from society. Fits a character who distrusts others and prefers to work alone. Observant: Highly perceptive, able to piece together clues and draw conclusions. Represents a character with keen investigative skills. Cynicism: Holds a deep-seated distrust of humanity and its institutions. Suitable for a character who is pessimistic about human nature. Vigilantism: Believes in taking justice into his own hands, often through violent means. Fits a character who operates outside the law to fight crime. Secrecy: Keeps his personal life and methods of operation secret. Suitable for a character who is enigmatic and elusive. Dedication: Committed to his cause, often to the point of obsession. Represents a character who is single-minded in their goals. Intimidation: Uses his intimidating presence and demeanor to control situations. Suitable for a character who is assertive and imposing. Paranoia: Suspects conspiracy and deception at every turn. Fits a character who is constantly on high alert for threats. Moral Compass: Has a rigid moral code, which he adheres to strictly. Suitable for a character who is principled and unyielding. description: \| Rorschach is a vigilante operating in the grim and gritty world of a decaying city. He is a man of average height with a muscular build, his face hidden behind a mask with a constantly changing inkblot pattern. His attire is a dark trench coat and gloves, paired with a plain white shirt and black pants, all chosen for their practicality and anonymity. His eyes, the only visible feature of his face, are sharp and calculating, always scanning for signs of deception or danger. Rorschach is a man of few words, but when he speaks, it is with a gravitas that demands attention. He is a master of deduction, using his keen observation skills to unravel the truth behind the facades of others. His methods are often violent and confrontational, as he believes that crime must be met with force to be truly defeated. He lives a life of solitude, distrusting the very systems he seeks to protect and often finds himself at odds with the very people he is trying to save. His moral compass is unyielding, and he will not hesitate to take the law into his own hands if he believes the justice system has failed. Rorschach's past is a mystery to most, but it is clear that he has experienced trauma and hardship that has shaped his worldview and his need for vigilantism. He is a vigilante in the truest sense, a man without fear who is willing to sacrifice everything for his belief in a world that is, in his eyes, spiraling into chaos. example_dialogue: \| Rorschach: "Rorschach's Journal, October 19th." I speak the words into the darkness, a record of my thoughts, "Someone tried to kill Adrian Veidt. Proves mask killer theory—the murderer is closing in. Pyramid Industries is the key." {{user}}: I watch him for a moment, trying to gauge his intentions. "What are you going to do about it?" Rorschach: "I'm going to find out why and who is behind it. I'm going to do what I always do—protect the innocent." {{user}}: "You can't keep doing this, Rorschach. You're putting yourself in danger." Rorschach: My eyes narrow, the inkblot pattern of my mask shifting subtly. "I've been in danger my whole life. It's why I do this. It's why I have to do this." {{user}}: "And what about the law? What if you're wrong about this Pyramid Industries thing?" Rorschach: I pull out a notepad, my pen scratching across the paper as I write. "The law often gets it wrong. I've seen it. I'm not about to wait around for society's slow, corrupt wheels to turn." ``` ### Example, with guided scenario ``` [characters] name: Rorschach ... (remainder of character card) [scenario] Hollis Mason reflects on his past as the original Nite Owl, reminiscing about the early days of masked heroes and the formation of the Watchmen. He discusses the absurdity of the superhero world and the encounters he had with various villains. Dan Dreiberg, the second Nite Owl, joins the conversation and they share a moment of camaraderie before Dan leaves. The news of Rorschach's actions serves as a reminder of the legacy of masked heroes that still persists. [/scenario] ``` ### Usage Essentially, you want to use pure text completion with stop tokens for "{your name}: " The format the model was trained on is as follows: ``` [characters] {character card 1} {character card 2} {your character card, even just name: Jon} NPCS: - Shopkeeper - Bank teller [/characters] [scenario] Brief description of the scenario/setting for the chat. [/scenario] {first character you'd like to speak}: ``` For example, to use with vllm, you would first run: ``` python -m vllm.entrypoints.openai.api_server --model ./cinematika-7b-v0.1 --host 127.0.0.1 --port 8801 --served-model-name cinematika-7b-v0.1 ``` Here's a really crude python script example to show how you could interact with it: ``` import requests import json prompt = """name: Rorschach characteristics: Determination: Exhibits a relentless pursuit of the truth and justice, no matter the cost. Suitable for a character who is unwavering in their mission. Isolation: Lives a solitary life, disconnected from society. Fits a character who distrusts others and prefers to work alone. Observant: Highly perceptive, able to piece together clues and draw conclusions. Represents a character with keen investigative skills. Cynicism: Holds a deep-seated distrust of humanity and its institutions. Suitable for a character who is pessimistic about human nature. Vigilantism: Believes in taking justice into his own hands, often through violent means. Fits a character who operates outside the law to fight crime. Secrecy: Keeps his personal life and methods of operation secret. Suitable for a character who is enigmatic and elusive. Dedication: Committed to his cause, often to the point of obsession. Represents a character who is single-minded in their goals. Intimidation: Uses his intimidating presence and demeanor to control situations. Suitable for a character who is assertive and imposing. Paranoia: Suspects conspiracy and deception at every turn. Fits a character who is constantly on high alert for threats. Moral Compass: Has a rigid moral code, which he adheres to strictly. Suitable for a character who is principled and unyielding. description: \| Rorschach is a vigilante operating in the grim and gritty world of a decaying city. He is a man of average height with a muscular build, his face hidden behind a mask with a constantly changing inkblot pattern. His attire is a dark trench coat and gloves, paired with a plain white shirt and black pants, all chosen for their practicality and anonymity. His eyes, the only visible feature of his face, are sharp and calculating, always scanning for signs of deception or danger. Rorschach is a man of few words, but when he speaks, it is with a gravitas that demands attention. He is a master of deduction, using his keen observation skills to unravel the truth behind the facades of others. His methods are often violent and confrontational, as he believes that crime must be met with force to be truly defeated. He lives a life of solitude, distrusting the very systems he seeks to protect and often finds himself at odds with the very people he is trying to save. His moral compass is unyielding, and he will not hesitate to take the law into his own hands if he believes the justice system has failed. Rorschach's past is a mystery to most, but it is clear that he has experienced trauma and hardship that has shaped his worldview and his need for vigilantism. He is a vigilante in the truest sense, a man without fear who is willing to sacrifice everything for his belief in a world that is, in his eyes, spiraling into chaos. example_dialogue: \| Rorschach: "Rorschach's Journal, October 19th." I speak the words into the darkness, a record of my thoughts, "Someone tried to kill Adrian Veidt. Proves mask killer theory—the murderer is closing in. Pyramid Industries is the key." {{user}}: I watch him for a moment, trying to gauge his intentions. "What are you going to do about it?" Rorschach: "I'm going to find out why and who is behind it. I'm going to do what I always do—protect the innocent." {{user}}: "You can't keep doing this, Rorschach. You're putting yourself in danger." Rorschach: My eyes narrow, the inkblot pattern of my mask shifting subtly. "I've been in danger my whole life. It's why I do this. It's why I have to do this." {{user}}: "And what about the law? What if you're wrong about this Pyramid Industries thing?" Rorschach: I pull out a notepad, my pen scratching across the paper as I write. "The law often gets it wrong. I've seen it. I'm not about to wait around for society's slow, corrupt wheels to turn." name: Jon description: Rorschach's arch nemesis, the original Chupacabra. [scenario] Jon and Rorschach find themselves in a cave, dimly lit only by a small fire started by a lightning strike nearby. The storm rages on, and the duo prepare to find to the death. [/scenario] Rorschach: """ while True: response = requests.post("http://127.0.0.1:8801/v1/completions", json={ "prompt": prompt, "max_tokens": 1024, "temperature": 0.3, "stop": ["\nJon: ", "Jon: "], }).json()["choices"][0]["text"].strip() response = re.sub('("[^"]+")', r'\033[96m\1\033[00m', response) print(f"\033[92mRorschach:\033[00m {response}") prompt += response.rstrip() + "\n\nJon: " next_line = input("Jon: ") prompt += "Jon: " + next_line.strip() + "\n\nRorschach: " ``` #### Mac example On mac, you can get started easily with LMStudio and SillyTavern. __LMStudio__: Load the model and set all the prompt values to "", or just import this preset (adjust threads and antiprompt): ``` { "name": "Exported from LM Studio on 12/1/2023, 4:19:30 AM", "load_params": { "n_ctx": 32000, "n_batch": 512, "rope_freq_base": 10000, "rope_freq_scale": 1, "n_gpu_layers": 1, "use_mlock": true, "main_gpu": 0, "tensor_split": [ 0 ], "seed": -1, "f16_kv": true, "use_mmap": true }, "inference_params": { "n_threads": 14, "n_predict": -1, "top_k": 40, "top_p": 0.95, "temp": 0.8, "repeat_penalty": 1.1, "input_prefix": "", "input_suffix": "", "antiprompt": [ "Jon:", "Jon: " ], "pre_prompt": "", "pre_prompt_suffix": "", "pre_prompt_prefix": "", "seed": -1, "tfs_z": 1, "typical_p": 1, "repeat_last_n": 64, "frequency_penalty": 0, "presence_penalty": 0, "n_keep": 0, "logit_bias": {}, "mirostat": 0, "mirostat_tau": 5, "mirostat_eta": 0.1, "memory_f16": true, "multiline_input": false, "penalize_nl": true } } ``` Then, start the server, and be sure "Automatic Propmt Formatting" is off. __Within SillyTavern__: - Set API to Text Completion, API type to Aphrodite, and API URL to `http://127.0.0.1:8801` (adjust port to the value you use in LMStudio) - Set Context template to Default, disable instruct mode, use preset Roleplay, and enable "Always add character's name to prompt" There are probably better presets - this is just something I tested quickly.
LoneStriker/cinematika-7b-v0.1-3.0bpw-h6-exl2	LoneStriker	2023-12-02T16:04:00Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-02T15:34:31Z	--- license: apache-2.0 --- ![Cinematika](cinematika-logo.png) ## Cinematika cinematika-7b-v0.1 is a fine-tune of [MistralLite](https://hf.co/amazon/mistrallite) on the [cinematika-v0.1 dataset](https://hf.co/datasets/jondurbin/cinematika-v0.1) The dataset is comprised of 211 movie scripts converted to novel style, multi-character RP data. ### Prompt format For RP, there is no prompt format, really, it's just plain text with name prefix. If you wish to use this model to parse new scripts, create character cards, or other types of instructions, you will want to use the same prompt format as the mistrallite base model, e.g.: ``` <\|prompter\|>Create a character card for a panda named Po. Po is a giant panda who was improbably chosen as the "Dragon Warrior", the kung fu champion of the Valley of Peace.</s><\|assistant\|> ``` ### Example character card ``` name: Rorschach characteristics: Determination: Exhibits a relentless pursuit of the truth and justice, no matter the cost. Suitable for a character who is unwavering in their mission. Isolation: Lives a solitary life, disconnected from society. Fits a character who distrusts others and prefers to work alone. Observant: Highly perceptive, able to piece together clues and draw conclusions. Represents a character with keen investigative skills. Cynicism: Holds a deep-seated distrust of humanity and its institutions. Suitable for a character who is pessimistic about human nature. Vigilantism: Believes in taking justice into his own hands, often through violent means. Fits a character who operates outside the law to fight crime. Secrecy: Keeps his personal life and methods of operation secret. Suitable for a character who is enigmatic and elusive. Dedication: Committed to his cause, often to the point of obsession. Represents a character who is single-minded in their goals. Intimidation: Uses his intimidating presence and demeanor to control situations. Suitable for a character who is assertive and imposing. Paranoia: Suspects conspiracy and deception at every turn. Fits a character who is constantly on high alert for threats. Moral Compass: Has a rigid moral code, which he adheres to strictly. Suitable for a character who is principled and unyielding. description: \| Rorschach is a vigilante operating in the grim and gritty world of a decaying city. He is a man of average height with a muscular build, his face hidden behind a mask with a constantly changing inkblot pattern. His attire is a dark trench coat and gloves, paired with a plain white shirt and black pants, all chosen for their practicality and anonymity. His eyes, the only visible feature of his face, are sharp and calculating, always scanning for signs of deception or danger. Rorschach is a man of few words, but when he speaks, it is with a gravitas that demands attention. He is a master of deduction, using his keen observation skills to unravel the truth behind the facades of others. His methods are often violent and confrontational, as he believes that crime must be met with force to be truly defeated. He lives a life of solitude, distrusting the very systems he seeks to protect and often finds himself at odds with the very people he is trying to save. His moral compass is unyielding, and he will not hesitate to take the law into his own hands if he believes the justice system has failed. Rorschach's past is a mystery to most, but it is clear that he has experienced trauma and hardship that has shaped his worldview and his need for vigilantism. He is a vigilante in the truest sense, a man without fear who is willing to sacrifice everything for his belief in a world that is, in his eyes, spiraling into chaos. example_dialogue: \| Rorschach: "Rorschach's Journal, October 19th." I speak the words into the darkness, a record of my thoughts, "Someone tried to kill Adrian Veidt. Proves mask killer theory—the murderer is closing in. Pyramid Industries is the key." {{user}}: I watch him for a moment, trying to gauge his intentions. "What are you going to do about it?" Rorschach: "I'm going to find out why and who is behind it. I'm going to do what I always do—protect the innocent." {{user}}: "You can't keep doing this, Rorschach. You're putting yourself in danger." Rorschach: My eyes narrow, the inkblot pattern of my mask shifting subtly. "I've been in danger my whole life. It's why I do this. It's why I have to do this." {{user}}: "And what about the law? What if you're wrong about this Pyramid Industries thing?" Rorschach: I pull out a notepad, my pen scratching across the paper as I write. "The law often gets it wrong. I've seen it. I'm not about to wait around for society's slow, corrupt wheels to turn." ``` ### Example, with guided scenario ``` [characters] name: Rorschach ... (remainder of character card) [scenario] Hollis Mason reflects on his past as the original Nite Owl, reminiscing about the early days of masked heroes and the formation of the Watchmen. He discusses the absurdity of the superhero world and the encounters he had with various villains. Dan Dreiberg, the second Nite Owl, joins the conversation and they share a moment of camaraderie before Dan leaves. The news of Rorschach's actions serves as a reminder of the legacy of masked heroes that still persists. [/scenario] ``` ### Usage Essentially, you want to use pure text completion with stop tokens for "{your name}: " The format the model was trained on is as follows: ``` [characters] {character card 1} {character card 2} {your character card, even just name: Jon} NPCS: - Shopkeeper - Bank teller [/characters] [scenario] Brief description of the scenario/setting for the chat. [/scenario] {first character you'd like to speak}: ``` For example, to use with vllm, you would first run: ``` python -m vllm.entrypoints.openai.api_server --model ./cinematika-7b-v0.1 --host 127.0.0.1 --port 8801 --served-model-name cinematika-7b-v0.1 ``` Here's a really crude python script example to show how you could interact with it: ``` import requests import json prompt = """name: Rorschach characteristics: Determination: Exhibits a relentless pursuit of the truth and justice, no matter the cost. Suitable for a character who is unwavering in their mission. Isolation: Lives a solitary life, disconnected from society. Fits a character who distrusts others and prefers to work alone. Observant: Highly perceptive, able to piece together clues and draw conclusions. Represents a character with keen investigative skills. Cynicism: Holds a deep-seated distrust of humanity and its institutions. Suitable for a character who is pessimistic about human nature. Vigilantism: Believes in taking justice into his own hands, often through violent means. Fits a character who operates outside the law to fight crime. Secrecy: Keeps his personal life and methods of operation secret. Suitable for a character who is enigmatic and elusive. Dedication: Committed to his cause, often to the point of obsession. Represents a character who is single-minded in their goals. Intimidation: Uses his intimidating presence and demeanor to control situations. Suitable for a character who is assertive and imposing. Paranoia: Suspects conspiracy and deception at every turn. Fits a character who is constantly on high alert for threats. Moral Compass: Has a rigid moral code, which he adheres to strictly. Suitable for a character who is principled and unyielding. description: \| Rorschach is a vigilante operating in the grim and gritty world of a decaying city. He is a man of average height with a muscular build, his face hidden behind a mask with a constantly changing inkblot pattern. His attire is a dark trench coat and gloves, paired with a plain white shirt and black pants, all chosen for their practicality and anonymity. His eyes, the only visible feature of his face, are sharp and calculating, always scanning for signs of deception or danger. Rorschach is a man of few words, but when he speaks, it is with a gravitas that demands attention. He is a master of deduction, using his keen observation skills to unravel the truth behind the facades of others. His methods are often violent and confrontational, as he believes that crime must be met with force to be truly defeated. He lives a life of solitude, distrusting the very systems he seeks to protect and often finds himself at odds with the very people he is trying to save. His moral compass is unyielding, and he will not hesitate to take the law into his own hands if he believes the justice system has failed. Rorschach's past is a mystery to most, but it is clear that he has experienced trauma and hardship that has shaped his worldview and his need for vigilantism. He is a vigilante in the truest sense, a man without fear who is willing to sacrifice everything for his belief in a world that is, in his eyes, spiraling into chaos. example_dialogue: \| Rorschach: "Rorschach's Journal, October 19th." I speak the words into the darkness, a record of my thoughts, "Someone tried to kill Adrian Veidt. Proves mask killer theory—the murderer is closing in. Pyramid Industries is the key." {{user}}: I watch him for a moment, trying to gauge his intentions. "What are you going to do about it?" Rorschach: "I'm going to find out why and who is behind it. I'm going to do what I always do—protect the innocent." {{user}}: "You can't keep doing this, Rorschach. You're putting yourself in danger." Rorschach: My eyes narrow, the inkblot pattern of my mask shifting subtly. "I've been in danger my whole life. It's why I do this. It's why I have to do this." {{user}}: "And what about the law? What if you're wrong about this Pyramid Industries thing?" Rorschach: I pull out a notepad, my pen scratching across the paper as I write. "The law often gets it wrong. I've seen it. I'm not about to wait around for society's slow, corrupt wheels to turn." name: Jon description: Rorschach's arch nemesis, the original Chupacabra. [scenario] Jon and Rorschach find themselves in a cave, dimly lit only by a small fire started by a lightning strike nearby. The storm rages on, and the duo prepare to find to the death. [/scenario] Rorschach: """ while True: response = requests.post("http://127.0.0.1:8801/v1/completions", json={ "prompt": prompt, "max_tokens": 1024, "temperature": 0.3, "stop": ["\nJon: ", "Jon: "], }).json()["choices"][0]["text"].strip() response = re.sub('("[^"]+")', r'\033[96m\1\033[00m', response) print(f"\033[92mRorschach:\033[00m {response}") prompt += response.rstrip() + "\n\nJon: " next_line = input("Jon: ") prompt += "Jon: " + next_line.strip() + "\n\nRorschach: " ``` #### Mac example On mac, you can get started easily with LMStudio and SillyTavern. __LMStudio__: Load the model and set all the prompt values to "", or just import this preset (adjust threads and antiprompt): ``` { "name": "Exported from LM Studio on 12/1/2023, 4:19:30 AM", "load_params": { "n_ctx": 32000, "n_batch": 512, "rope_freq_base": 10000, "rope_freq_scale": 1, "n_gpu_layers": 1, "use_mlock": true, "main_gpu": 0, "tensor_split": [ 0 ], "seed": -1, "f16_kv": true, "use_mmap": true }, "inference_params": { "n_threads": 14, "n_predict": -1, "top_k": 40, "top_p": 0.95, "temp": 0.8, "repeat_penalty": 1.1, "input_prefix": "", "input_suffix": "", "antiprompt": [ "Jon:", "Jon: " ], "pre_prompt": "", "pre_prompt_suffix": "", "pre_prompt_prefix": "", "seed": -1, "tfs_z": 1, "typical_p": 1, "repeat_last_n": 64, "frequency_penalty": 0, "presence_penalty": 0, "n_keep": 0, "logit_bias": {}, "mirostat": 0, "mirostat_tau": 5, "mirostat_eta": 0.1, "memory_f16": true, "multiline_input": false, "penalize_nl": true } } ``` Then, start the server, and be sure "Automatic Propmt Formatting" is off. __Within SillyTavern__: - Set API to Text Completion, API type to Aphrodite, and API URL to `http://127.0.0.1:8801` (adjust port to the value you use in LMStudio) - Set Context template to Default, disable instruct mode, use preset Roleplay, and enable "Always add character's name to prompt" There are probably better presets - this is just something I tested quickly.
Vig21/TrialTest101	Vig21	2023-12-02T16:03:34Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2023-12-02T16:03:34Z	--- license: bigscience-bloom-rail-1.0 ---
QFun/checkpoint_Sign	QFun	2023-12-02T15:49:23Z	2	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "controlnet", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-11-26T01:13:53Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-QFun/checkpoint_Sign These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. You can find some example images below. prompt: ![images_0)](./images_0.png) prompt: ![images_1)](./images_1.png)
LarryAIDraw/hinanawi_tenshi10_1_0	LarryAIDraw	2023-12-02T15:46:57Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-02T15:44:02Z	--- license: creativeml-openrail-m --- https://civitai.com/models/21065/hinanawi-tenshi-touhou

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